Workload Placement Based On Carbon Emissions

ABSTRACT

Workload placement based on carbon emissions, including: calculating, for each execution environment of a plurality of execution environments, a carbon emission cost associated with a workload; selecting, based on each carbon emission cost for the plurality of execution environments, a target execution environment; and executing the workload on the target execution environment.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation-in-part application for patent entitled to afiling date and claiming the benefit of earlier-filed U.S. PatentApplication No. 17/078,392, filed Oct. 23, 2020, herein incorporated byreference in its entirety, which is a continuation of and claimspriority from U.S. Pat. No. 10,853,148, issued Dec. 1, 2020, which is acontinuation-in-part application of and claims priority from U.S. patentapplication Ser. No. 15/987,875, filed May 23, 2018, which claims thebenefit of: U.S. Provisional Patent Application No. 62/518,146, filedJun. 12, 2017, U.S. Provisional Patent Application No. 62/549,399, filedAug. 23, 2017, U.S. Provisional Patent Application No. 62/575,966, filedOct. 23, 2017, and U.S. Provisional Patent Application No. 62/674,688,filed May 22, 2018.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A illustrates a first example system for data storage inaccordance with some implementations.

FIG. 1B illustrates a second example system for data storage inaccordance with some implementations.

FIG. 1C illustrates a third example system for data storage inaccordance with some implementations.

FIG. 1D illustrates a fourth example system for data storage inaccordance with some implementations.

FIG. 2A is a perspective view of a storage cluster with multiple storagenodes and internal storage coupled to each storage node to providenetwork attached storage, in accordance with some embodiments.

FIG. 2B is a block diagram showing an interconnect switch couplingmultiple storage nodes in accordance with some embodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode and contents of one of the non-volatile solid state storage unitsin accordance with some embodiments.

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes and storage units of some previous figures inaccordance with some embodiments.

FIG. 2E is a blade hardware block diagram, showing a control plane,compute and storage planes, and authorities interacting with underlyingphysical resources, in accordance with some embodiments.

FIG. 2F depicts elasticity software layers in blades of a storagecluster, in accordance with some embodiments.

FIG. 2G depicts authorities and storage resources in blades of a storagecluster, in accordance with some embodiments.

FIG. 3A sets forth a diagram of a storage system that is coupled fordata communications with a cloud services provider in accordance withsome embodiments of the present disclosure.

FIG. 3B sets forth a diagram of a storage system in accordance with someembodiments of the present disclosure.

FIG. 3C sets forth an example of a cloud-based storage system inaccordance with some embodiments of the present disclosure.

FIG. 3D illustrates an exemplary computing device that may bespecifically configured to perform one or more of the processesdescribed herein.

FIG. 4 sets forth a flowchart illustrating an example method of workloadplanning in a storage system according to some embodiments of thepresent disclosure.

FIG. 5 sets forth a flowchart illustrating an additional example methodof workload planning in a storage system according to some embodimentsof the present disclosure.

FIG. 6 sets forth a flowchart illustrating an additional example methodof workload planning in a storage system according to some embodimentsof the present disclosure.

FIG. 7 sets forth a flowchart illustrating an additional example methodof workload planning in a storage system according to some embodimentsof the present disclosure.

FIG. 8 sets forth a flowchart illustrating an additional example methodof workload planning in a storage system according to some embodimentsof the present disclosure.

FIG. 9 sets forth a flowchart illustrating an additional example methodof workload planning in a storage system according to some embodimentsof the present disclosure.

FIG. 10 sets forth a flowchart illustrating an example method ofmigrating workloads between a plurality of execution environmentsaccording to some embodiments of the present disclosure.

FIG. 11 sets forth a flowchart illustrating an additional example methodof migrating workloads between a plurality of execution environmentsaccording to some embodiments of the present disclosure.

FIG. 12 sets forth a flowchart illustrating an additional example methodof migrating workloads between a plurality of execution environmentsaccording to some embodiments of the present disclosure.

FIG. 13 sets forth a flowchart illustrating an additional example methodof migrating workloads between a plurality of execution environmentsaccording to some embodiments of the present disclosure.

FIG. 14 sets forth a flowchart illustrating an additional example methodof migrating workloads between a plurality of execution environmentsaccording to some embodiments of the present disclosure.

FIG. 15 sets forth a flowchart illustrating an additional example methodof migrating workloads between a plurality of execution environmentsaccording to some embodiments of the present disclosure.

FIG. 16 sets forth a flowchart illustrating an additional example methodof migrating workloads between a plurality of execution environmentsaccording to some embodiments of the present disclosure.

FIG. 17 sets forth a flowchart illustrating an example method ofworkload placement based on carbon emissions according to someembodiments of the present disclosure.

FIG. 18 sets forth a flowchart illustrating another example method ofworkload placement based on carbon emissions according to someembodiments of the present disclosure.

FIG. 19 sets forth a flowchart illustrating another example method ofworkload placement based on carbon emissions according to someembodiments of the present disclosure.

FIG. 20 sets forth a flowchart illustrating another example method ofworkload placement based on carbon emissions according to someembodiments of the present disclosure.

FIG. 21 sets forth a flowchart illustrating another example method ofworkload placement based on carbon emissions according to someembodiments of the present disclosure.

FIG. 22 sets forth a flowchart illustrating another example method ofworkload placement based on carbon emissions according to someembodiments of the present disclosure.

DESCRIPTION OF EMBODIMENTS

Example methods, apparatus, and products for migrating workloads betweena plurality of execution environments in accordance with embodiments ofthe present disclosure are described with reference to the accompanyingdrawings, beginning with FIG. 1A. FIG. 1A illustrates an example systemfor data storage, in accordance with some implementations. System 100(also referred to as “storage system” herein) includes numerous elementsfor purposes of illustration rather than limitation. It may be notedthat system 100 may include the same, more, or fewer elements configuredin the same or different manner in other implementations.

System 100 includes a number of computing devices 164A-B. Computingdevices (also referred to as “client devices” herein) may be embodied,for example, a server in a data center, a workstation, a personalcomputer, a notebook, or the like. Computing devices 164A-B may becoupled for data communications to one or more storage arrays 102A-Bthrough a storage area network (‘SAN’) 158 or a local area network(‘LAN’) 160.

The SAN 158 may be implemented with a variety of data communicationsfabrics, devices, and protocols. For example, the fabrics for SAN 158may include Fibre Channel, Ethernet, Infiniband, Serial Attached SmallComputer System Interface (‘SAS’), or the like. Data communicationsprotocols for use with SAN 158 may include Advanced TechnologyAttachment (‘ATA’), Fibre Channel Protocol, Small Computer SystemInterface (‘SCSI’), Internet Small Computer System Interface (‘iSCSI’),HyperSCSI, Non-Volatile Memory Express (‘NVMe’) over Fabrics, or thelike. It may be noted that SAN 158 is provided for illustration, ratherthan limitation. Other data communication couplings may be implementedbetween computing devices 164A-B and storage arrays 102A-B.

The LAN 160 may also be implemented with a variety of fabrics, devices,and protocols. For example, the fabrics for LAN 160 may include Ethernet(802.3), wireless (802.11), or the like. Data communication protocolsfor use in LAN 160 may include Transmission Control Protocol (‘TCP’),User Datagram Protocol (‘UDP’), Internet Protocol (‘IP’), HyperTextTransfer Protocol (‘HTTP’), Wireless Access Protocol (‘WAP’), HandheldDevice Transport Protocol (‘HDTP’), Session Initiation Protocol (‘SIP’),Real Time Protocol (‘RTP’), or the like.

Storage arrays 102A-B may provide persistent data storage for thecomputing devices 164A-B. Storage array 102A may be contained in achassis (not shown), and storage array 102B may be contained in anotherchassis (not shown), in implementations. Storage array 102A and 102B mayinclude one or more storage array controllers 110A-D (also referred toas “controller” herein). A storage array controller 110A-D may beembodied as a module of automated computing machinery comprisingcomputer hardware, computer software, or a combination of computerhardware and software. In some implementations, the storage arraycontrollers 110A-D may be configured to carry out various storage tasks.Storage tasks may include writing data received from the computingdevices 164A-B to storage array 102A-B, erasing data from storage array102A-B, retrieving data from storage array 102A-B and providing data tocomputing devices 164A-B, monitoring and reporting of disk utilizationand performance, performing redundancy operations, such as RedundantArray of Independent Drives (‘RAID’) or RAID-like data redundancyoperations, compressing data, encrypting data, and so forth.

Storage array controller 110A-D may be implemented in a variety of ways,including as a Field Programmable Gate Array (‘FPGA’), a ProgrammableLogic Chip (‘PLC’), an Application Specific Integrated Circuit (‘ASIC’),System-on-Chip (‘SOC’), or any computing device that includes discretecomponents such as a processing device, central processing unit,computer memory, or various adapters. Storage array controller 110A-Dmay include, for example, a data communications adapter configured tosupport communications via the SAN 158 or LAN 160. In someimplementations, storage array controller 110A-D may be independentlycoupled to the LAN 160. In implementations, storage array controller110A-D may include an I/O controller or the like that couples thestorage array controller 110A-D for data communications, through amidplane (not shown), to a persistent storage resource 170A-B (alsoreferred to as a “storage resource” herein). The persistent storageresource 170A-B main include any number of storage drives 171A-F (alsoreferred to as “storage devices” herein) and any number of non-volatileRandom Access Memory (‘NVRAM’) devices (not shown).

In some implementations, the NVRAM devices of a persistent storageresource 170A-B may be configured to receive, from the storage arraycontroller 110A-D, data to be stored in the storage drives 171A-F. Insome examples, the data may originate from computing devices 164A-B. Insome examples, writing data to the NVRAM device may be carried out morequickly than directly writing data to the storage drive 171A-F. Inimplementations, the storage array controller 110A-D may be configuredto utilize the NVRAM devices as a quickly accessible buffer for datadestined to be written to the storage drives 171A-F. Latency for writerequests using NVRAM devices as a buffer may be improved relative to asystem in which a storage array controller 110A-D writes data directlyto the storage drives 171A-F. In some implementations, the NVRAM devicesmay be implemented with computer memory in the form of high bandwidth,low latency RAM. The NVRAM device is referred to as “non-volatile”because the NVRAM device may receive or include a unique power sourcethat maintains the state of the RAM after main power loss to the NVRAMdevice. Such a power source may be a battery, one or more capacitors, orthe like. In response to a power loss, the NVRAM device may beconfigured to write the contents of the RAM to a persistent storage,such as the storage drives 171A-F.

In implementations, storage drive 171A-F may refer to any deviceconfigured to record data persistently, where “persistently” or“persistent” refers as to a device's ability to maintain recorded dataafter loss of power. In some implementations, storage drive 171A-F maycorrespond to non-disk storage media. For example, the storage drive171A-F may be one or more solid-state drives (‘SSDs’), flash memorybased storage, any type of solid-state non-volatile memory, or any othertype of non-mechanical storage device. In other implementations, storagedrive 171A-F may include mechanical or spinning hard disk, such ashard-disk drives (‘HDD’).

In some implementations, the storage array controllers 110A-D may beconfigured for offloading device management responsibilities fromstorage drive 171A-F in storage array 102A-B. For example, storage arraycontrollers 110A-D may manage control information that may describe thestate of one or more memory blocks in the storage drives 171A-F. Thecontrol information may indicate, for example, that a particular memoryblock has failed and should no longer be written to, that a particularmemory block contains boot code for a storage array controller 110A-D,the number of program-erase (‘PIE’) cycles that have been performed on aparticular memory block, the age of data stored in a particular memoryblock, the type of data that is stored in a particular memory block, andso forth. In some implementations, the control information may be storedwith an associated memory block as metadata. In other implementations,the control information for the storage drives 171A-F may be stored inone or more particular memory blocks of the storage drives 171A-F thatare selected by the storage array controller 110A-D. The selected memoryblocks may be tagged with an identifier indicating that the selectedmemory block contains control information. The identifier may beutilized by the storage array controllers 110A-D in conjunction withstorage drives 171A-F to quickly identify the memory blocks that containcontrol information. For example, the storage controllers 110A-D mayissue a command to locate memory blocks that contain controlinformation. It may be noted that control information may be so largethat parts of the control information may be stored in multiplelocations, that the control information may be stored in multiplelocations for purposes of redundancy, for example, or that the controlinformation may otherwise be distributed across multiple memory blocksin the storage drive 171A-F.

In implementations, storage array controllers 110A-D may offload devicemanagement responsibilities from storage drives 171A-F of storage array102A-B by retrieving, from the storage drives 171A-F, controlinformation describing the state of one or more memory blocks in thestorage drives 171A-F. Retrieving the control information from thestorage drives 171A-F may be carried out, for example, by the storagearray controller 110A-D querying the storage drives 171A-F for thelocation of control information for a particular storage drive 171A-F.The storage drives 171A-F may be configured to execute instructions thatenable the storage drive 171A-F to identify the location of the controlinformation. The instructions may be executed by a controller (notshown) associated with or otherwise located on the storage drive 171A-Fand may cause the storage drive 171A-F to scan a portion of each memoryblock to identify the memory blocks that store control information forthe storage drives 171A-F. The storage drives 171A-F may respond bysending a response message to the storage array controller 110A-D thatincludes the location of control information for the storage drive171A-F. Responsive to receiving the response message, storage arraycontrollers 110A-D may issue a request to read data stored at theaddress associated with the location of control information for thestorage drives 171A-F.

In other implementations, the storage array controllers 110A-D mayfurther offload device management responsibilities from storage drives171A-F by performing, in response to receiving the control information,a storage drive management operation. A storage drive managementoperation may include, for example, an operation that is typicallyperformed by the storage drive 171A-F (e.g., the controller (not shown)associated with a particular storage drive 171A-F). A storage drivemanagement operation may include, for example, ensuring that data is notwritten to failed memory blocks within the storage drive 171A-F,ensuring that data is written to memory blocks within the storage drive171A-F in such a way that adequate wear leveling is achieved, and soforth.

In implementations, storage array 102A-B may implement two or morestorage array controllers 110A-D. For example, storage array 102A mayinclude storage array controllers 110A and storage array controllers110B. At a given instance, a single storage array controller 110A-D(e.g., storage array controller 110A) of a storage system 100 may bedesignated with primary status (also referred to as “primary controller”herein), and other storage array controllers 110A-D (e.g., storage arraycontroller 110A) may be designated with secondary status (also referredto as “secondary controller” herein). The primary controller may haveparticular rights, such as permission to alter data in persistentstorage resource 170A-B (e.g., writing data to persistent storageresource 170A-B). At least some of the rights of the primary controllermay supersede the rights of the secondary controller. For instance, thesecondary controller may not have permission to alter data in persistentstorage resource 170A-B when the primary controller has the right. Thestatus of storage array controllers 110A-D may change. For example,storage array controller 110A may be designated with secondary status,and storage array controller 110B may be designated with primary status.

In some implementations, a primary controller, such as storage arraycontroller 110A, may serve as the primary controller for one or morestorage arrays 102A-B, and a second controller, such as storage arraycontroller 110B, may serve as the secondary controller for the one ormore storage arrays 102A-B. For example, storage array controller 110Amay be the primary controller for storage array 102A and storage array102B, and storage array controller 110B may be the secondary controllerfor storage array 102A and 102B. In some implementations, storage arraycontrollers 110C and 110D (also referred to as “storage processingmodules”) may neither have primary or secondary status. Storage arraycontrollers 110C and 110D, implemented as storage processing modules,may act as a communication interface between the primary and secondarycontrollers (e.g., storage array controllers 110A and 110B,respectively) and storage array 102B. For example, storage arraycontroller 110A of storage array 102A may send a write request, via SAN158, to storage array 102B. The write request may be received by bothstorage array controllers 110C and 110D of storage array 102B. Storagearray controllers 110C and 110D facilitate the communication, e.g., sendthe write request to the appropriate storage drive 171A-F. It may benoted that in some implementations storage processing modules may beused to increase the number of storage drives controlled by the primaryand secondary controllers.

In implementations, storage array controllers 110A-D are communicativelycoupled, via a midplane (not shown), to one or more storage drives171A-F and to one or more NVRAM devices (not shown) that are included aspart of a storage array 102A-B. The storage array controllers 110A-D maybe coupled to the midplane via one or more data communication links andthe midplane may be coupled to the storage drives 171A-F and the NVRAMdevices via one or more data communications links. The datacommunications links described herein are collectively illustrated bydata communications links 108A-D and may include a Peripheral ComponentInterconnect Express (‘PCIe’) bus, for example.

FIG. 1B illustrates an example system for data storage, in accordancewith some implementations. Storage array controller 101 illustrated inFIG. 1B may be similar to the storage array controllers 110A-D describedwith respect to FIG. 1A. In one example, storage array controller 101may be similar to storage array controller 110A or storage arraycontroller 110B. Storage array controller 101 includes numerous elementsfor purposes of illustration rather than limitation. It may be notedthat storage array controller 101 may include the same, more, or fewerelements configured in the same or different manner in otherimplementations. It may be noted that elements of FIG. 1A may beincluded below to help illustrate features of storage array controller101.

Storage array controller 101 may include one or more processing devices104 and random access memory (‘RAM’) 111. Processing device 104 (orcontroller 101) represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 104 (or controller 101) may bea complex instruction set computing (‘CISC’) microprocessor, reducedinstruction set computing (‘RISC’) microprocessor, very long instructionword (‘VLIW’) microprocessor, or a processor implementing otherinstruction sets or processors implementing a combination of instructionsets. The processing device 104 (or controller 101) may also be one ormore special-purpose processing devices such as an ASIC, an FPGA, adigital signal processor (‘DSP’), network processor, or the like.

The processing device 104 may be connected to the RAM 111 via a datacommunications link 106, which may be embodied as a high speed memorybus such as a Double-Data Rate 4 (‘DDR4’) bus. Stored in RAM 111 is anoperating system 112. In some implementations, instructions 113 arestored in RAM 111. Instructions 113 may include computer programinstructions for performing operations in in a direct-mapped flashstorage system. In one embodiment, a direct-mapped flash storage systemis one that that addresses data blocks within flash drives directly andwithout an address translation performed by the storage controllers ofthe flash drives.

In implementations, storage array controller 101 includes one or morehost bus adapters 103A-C that are coupled to the processing device 104via a data communications link 105A-C. In implementations, host busadapters 103A-C may be computer hardware that connects a host system(e.g., the storage array controller) to other network and storagearrays. In some examples, host bus adapters 103A-C may be a FibreChannel adapter that enables the storage array controller 101 to connectto a SAN, an Ethernet adapter that enables the storage array controller101 to connect to a LAN, or the like. Host bus adapters 103A-C may becoupled to the processing device 104 via a data communications link105A-C such as, for example, a PCIe bus.

In implementations, storage array controller 101 may include a host busadapter 114 that is coupled to an expander 115. The expander 115 may beused to attach a host system to a larger number of storage drives. Theexpander 115 may, for example, be a SAS expander utilized to enable thehost bus adapter 114 to attach to storage drives in an implementationwhere the host bus adapter 114 is embodied as a SAS controller.

In implementations, storage array controller 101 may include a switch116 coupled to the processing device 104 via a data communications link109. The switch 116 may be a computer hardware device that can createmultiple endpoints out of a single endpoint, thereby enabling multipledevices to share a single endpoint. The switch 116 may, for example, bea PCIe switch that is coupled to a PCIe bus (e.g., data communicationslink 109) and presents multiple PCIe connection points to the midplane.

In implementations, storage array controller 101 includes a datacommunications link 107 for coupling the storage array controller 101 toother storage array controllers. In some examples, data communicationslink 107 may be a QuickPath Interconnect (QPI) interconnect.

A traditional storage system that uses traditional flash drives mayimplement a process across the flash drives that are part of thetraditional storage system. For example, a higher level process of thestorage system may initiate and control a process across the flashdrives. However, a flash drive of the traditional storage system mayinclude its own storage controller that also performs the process. Thus,for the traditional storage system, a higher level process (e.g.,initiated by the storage system) and a lower level process (e.g.,initiated by a storage controller of the storage system) may both beperformed.

To resolve various deficiencies of a traditional storage system,operations may be performed by higher level processes and not by thelower level processes. For example, the flash storage system may includeflash drives that do not include storage controllers that provide theprocess. Thus, the operating system of the flash storage system itselfmay initiate and control the process. This may be accomplished by adirect-mapped flash storage system that addresses data blocks within theflash drives directly and without an address translation performed bythe storage controllers of the flash drives.

In implementations, storage drive 171A-F may be one or more zonedstorage devices. In some implementations, the one or more zoned storagedevices may be a shingled HDD. In implementations, the one or morestorage devices may be a flash-based SSD. In a zoned storage device, azoned namespace on the zoned storage device can be addressed by groupsof blocks that are grouped and aligned by a natural size, forming anumber of addressable zones. In implementations utilizing an SSD, thenatural size may be based on the erase block size of the SSD. In someimplementations, the zones of the zoned storage device may be definedduring initialization of the zoned storage device. In implementations,the zones may be defined dynamically as data is written to the zonedstorage device.

In some implementations, zones may be heterogeneous, with some zoneseach being a page group and other zones being multiple page groups. Inimplementations, some zones may correspond to an erase block and otherzones may correspond to multiple erase blocks. In an implementation,zones may be any combination of differing numbers of pages in pagegroups and/or erase blocks, for heterogeneous mixes of programmingmodes, manufacturers, product types and/or product generations ofstorage devices, as applied to heterogeneous assemblies, upgrades,distributed storages, etc. In some implementations, zones may be definedas having usage characteristics, such as a property of supporting datawith particular kinds of longevity (very short lived or very long lived,for example). These properties could be used by a zoned storage deviceto determine how the zone will be managed over the zone's expectedlifetime.

It should be appreciated that a zone is a virtual construct. Anyparticular zone may not have a fixed location at a storage device. Untilallocated, a zone may not have any location at a storage device. A zonemay correspond to a number representing a chunk of virtually allocatablespace that is the size of an erase block or other block size in variousimplementations. When the system allocates or opens a zone, zones getallocated to flash or other solid-state storage memory and, as thesystem writes to the zone, pages are written to that mapped flash orother solid-state storage memory of the zoned storage device. When thesystem closes the zone, the associated erase block(s) or other sizedblock(s) are completed. At some point in the future, the system maydelete a zone which will free up the zone's allocated space. During itslifetime, a zone may be moved around to different locations of the zonedstorage device, e.g., as the zoned storage device does internalmaintenance.

In implementations, the zones of the zoned storage device may be indifferent states. A zone may be in an empty state in which data has notbeen stored at the zone. An empty zone may be opened explicitly, orimplicitly by writing data to the zone. This is the initial state forzones on a fresh zoned storage device, but may also be the result of azone reset. In some implementations, an empty zone may have a designatedlocation within the flash memory of the zoned storage device. In animplementation, the location of the empty zone may be chosen when thezone is first opened or first written to (or later if writes arebuffered into memory). A zone may be in an open state either implicitlyor explicitly, where a zone that is in an open state may be written tostore data with write or append commands. In an implementation, a zonethat is in an open state may also be written to using a copy commandthat copies data from a different zone. In some implementations, a zonedstorage device may have a limit on the number of open zones at aparticular time.

A zone in a closed state is a zone that has been partially written to,but has entered a closed state after issuing an explicit closeoperation. A zone in a closed state may be left available for futurewrites, but may reduce some of the run-time overhead consumed by keepingthe zone in an open state. In implementations, a zoned storage devicemay have a limit on the number of closed zones at a particular time. Azone in a full state is a zone that is storing data and can no longer bewritten to. A zone may be in a full state either after writes havewritten data to the entirety of the zone or as a result of a zone finishoperation. Prior to a finish operation, a zone may or may not have beencompletely written. After a finish operation, however, the zone may notbe opened a written to further without first performing a zone resetoperation.

The mapping from a zone to an erase block (or to a shingled track in anHDD) may be arbitrary, dynamic, and hidden from view. The process ofopening a zone may be an operation that allows a new zone to bedynamically mapped to underlying storage of the zoned storage device,and then allows data to be written through appending writes into thezone until the zone reaches capacity. The zone can be finished at anypoint, after which further data may not be written into the zone. Whenthe data stored at the zone is no longer needed, the zone can be resetwhich effectively deletes the zone's content from the zoned storagedevice, making the physical storage held by that zone available for thesubsequent storage of data. Once a zone has been written and finished,the zoned storage device ensures that the data stored at the zone is notlost until the zone is reset. In the time between writing the data tothe zone and the resetting of the zone, the zone may be moved aroundbetween shingle tracks or erase blocks as part of maintenance operationswithin the zoned storage device, such as by copying data to keep thedata refreshed or to handle memory cell aging in an SSD.

In implementations utilizing an HDD, the resetting of the zone may allowthe shingle tracks to be allocated to a new, opened zone that may beopened at some point in the future. In implementations utilizing an SSD,the resetting of the zone may cause the associated physical eraseblock(s) of the zone to be erased and subsequently reused for thestorage of data. In some implementations, the zoned storage device mayhave a limit on the number of open zones at a point in time to reducethe amount of overhead dedicated to keeping zones open.

The operating system of the flash storage system may identify andmaintain a list of allocation units across multiple flash drives of theflash storage system. The allocation units may be entire erase blocks ormultiple erase blocks. The operating system may maintain a map oraddress range that directly maps addresses to erase blocks of the flashdrives of the flash storage system.

Direct mapping to the erase blocks of the flash drives may be used torewrite data and erase data. For example, the operations may beperformed on one or more allocation units that include a first data anda second data where the first data is to be retained and the second datais no longer being used by the flash storage system. The operatingsystem may initiate the process to write the first data to new locationswithin other allocation units and erasing the second data and markingthe allocation units as being available for use for subsequent data.Thus, the process may only be performed by the higher level operatingsystem of the flash storage system without an additional lower levelprocess being performed by controllers of the flash drives.

Advantages of the process being performed only by the operating systemof the flash storage system include increased reliability of the flashdrives of the flash storage system as unnecessary or redundant writeoperations are not being performed during the process. One possiblepoint of novelty here is the concept of initiating and controlling theprocess at the operating system of the flash storage system. Inaddition, the process can be controlled by the operating system acrossmultiple flash drives. This is contrast to the process being performedby a storage controller of a flash drive.

A storage system can consist of two storage array controllers that sharea set of drives for failover purposes, or it could consist of a singlestorage array controller that provides a storage service that utilizesmultiple drives, or it could consist of a distributed network of storagearray controllers each with some number of drives or some amount ofFlash storage where the storage array controllers in the networkcollaborate to provide a complete storage service and collaborate onvarious aspects of a storage service including storage allocation andgarbage collection.

FIG. 1C illustrates a third example system 117 for data storage inaccordance with some implementations. System 117 (also referred to as“storage system” herein) includes numerous elements for purposes ofillustration rather than limitation. It may be noted that system 117 mayinclude the same, more, or fewer elements configured in the same ordifferent manner in other implementations.

In one embodiment, system 117 includes a dual Peripheral ComponentInterconnect (‘PCI’) flash storage device 118 with separatelyaddressable fast write storage. System 117 may include a storage devicecontroller 119. In one embodiment, storage device controller 119A-D maybe a CPU, ASIC, FPGA, or any other circuitry that may implement controlstructures necessary according to the present disclosure. In oneembodiment, system 117 includes flash memory devices (e.g., includingflash memory devices 120 a-n), operatively coupled to various channelsof the storage device controller 119. Flash memory devices 120 a-n, maybe presented to the controller 119A-D as an addressable collection ofFlash pages, erase blocks, and/or control elements sufficient to allowthe storage device controller 119A-D to program and retrieve variousaspects of the Flash. In one embodiment, storage device controller119A-D may perform operations on flash memory devices 120 a-n includingstoring and retrieving data content of pages, arranging and erasing anyblocks, tracking statistics related to the use and reuse of Flash memorypages, erase blocks, and cells, tracking and predicting error codes andfaults within the Flash memory, controlling voltage levels associatedwith programming and retrieving contents of Flash cells, etc.

In one embodiment, system 117 may include RAM 121 to store separatelyaddressable fast-write data. In one embodiment, RAM 121 may be one ormore separate discrete devices. In another embodiment, RAM 121 may beintegrated into storage device controller 119A-D or multiple storagedevice controllers. The RAM 121 may be utilized for other purposes aswell, such as temporary program memory for a processing device (e.g., aCPU) in the storage device controller 119.

In one embodiment, system 117 may include a stored energy device 122,such as a rechargeable battery or a capacitor. Stored energy device 122may store energy sufficient to power the storage device controller 119,some amount of the RAM (e.g., RAM 121), and some amount of Flash memory(e.g., Flash memory 120 a-120 n) for sufficient time to write thecontents of RAM to Flash memory. In one embodiment, storage devicecontroller 119A-D may write the contents of RAM to Flash Memory if thestorage device controller detects loss of external power.

In one embodiment, system 117 includes two data communications links 123a, 123 b. In one embodiment, data communications links 123 a, 123 b maybe PCI interfaces. In another embodiment, data communications links 123a, 123 b may be based on other communications standards (e.g.,HyperTransport, InfiniBand, etc.). Data communications links 123 a, 123b may be based on non-volatile memory express (‘NVMe’) or NVMe overfabrics (‘NVMf’) specifications that allow external connection to thestorage device controller 119A-D from other components in the storagesystem 117. It should be noted that data communications links may beinterchangeably referred to herein as PCI buses for convenience.

System 117 may also include an external power source (not shown), whichmay be provided over one or both data communications links 123 a, 123 b,or which may be provided separately. An alternative embodiment includesa separate Flash memory (not shown) dedicated for use in storing thecontent of RAM 121. The storage device controller 119A-D may present alogical device over a PCI bus which may include an addressablefast-write logical device, or a distinct part of the logical addressspace of the storage device 118, which may be presented as PCI memory oras persistent storage. In one embodiment, operations to store into thedevice are directed into the RAM 121. On power failure, the storagedevice controller 119A-D may write stored content associated with theaddressable fast-write logical storage to Flash memory (e.g., Flashmemory 120 a-n) for long-term persistent storage.

In one embodiment, the logical device may include some presentation ofsome or all of the content of the Flash memory devices 120 a-n, wherethat presentation allows a storage system including a storage device 118(e.g., storage system 117) to directly address Flash memory pages anddirectly reprogram erase blocks from storage system components that areexternal to the storage device through the PCI bus. The presentation mayalso allow one or more of the external components to control andretrieve other aspects of the Flash memory including some or all of:tracking statistics related to use and reuse of Flash memory pages,erase blocks, and cells across all the Flash memory devices; trackingand predicting error codes and faults within and across the Flash memorydevices; controlling voltage levels associated with programming andretrieving contents of Flash cells; etc.

In one embodiment, the stored energy device 122 may be sufficient toensure completion of in-progress operations to the Flash memory devices120 a-120 n stored energy device 122 may power storage device controller119A-D and associated Flash memory devices (e.g., 120 a-n) for thoseoperations, as well as for the storing of fast-write RAM to Flashmemory. Stored energy device 122 may be used to store accumulatedstatistics and other parameters kept and tracked by the Flash memorydevices 120 a-n and/or the storage device controller 119. Separatecapacitors or stored energy devices (such as smaller capacitors near orembedded within the Flash memory devices themselves) may be used forsome or all of the operations described herein.

Various schemes may be used to track and optimize the life span of thestored energy component, such as adjusting voltage levels over time,partially discharging the stored energy device 122 to measurecorresponding discharge characteristics, etc. If the available energydecreases over time, the effective available capacity of the addressablefast-write storage may be decreased to ensure that it can be writtensafely based on the currently available stored energy.

FIG. 1D illustrates a third example storage system 124 for data storagein accordance with some implementations. In one embodiment, storagesystem 124 includes storage controllers 125 a, 125 b. In one embodiment,storage controllers 125 a, 125 b are operatively coupled to Dual PCIstorage devices. Storage controllers 125 a, 125 b may be operativelycoupled (e.g., via a storage network 130) to some number of hostcomputers 127 a-n.

In one embodiment, two storage controllers (e.g., 125 a and 125 b)provide storage services, such as a SCS) block storage array, a fileserver, an object server, a database or data analytics service, etc. Thestorage controllers 125 a, 125 b may provide services through somenumber of network interfaces (e.g., 126 a-d) to host computers 127 a-noutside of the storage system 124. Storage controllers 125 a, 125 b mayprovide integrated services or an application entirely within thestorage system 124, forming a converged storage and compute system. Thestorage controllers 125 a, 125 b may utilize the fast write memorywithin or across storage devices 119 a-d to journal in progressoperations to ensure the operations are not lost on a power failure,storage controller removal, storage controller or storage systemshutdown, or some fault of one or more software or hardware componentswithin the storage system 124.

In one embodiment, storage controllers 125 a, 125 b operate as PCImasters to one or the other PCI buses 128 a, 128 b. In anotherembodiment, 128 a and 128 b may be based on other communicationsstandards (e.g., HyperTransport, InfiniBand, etc.). Other storage systemembodiments may operate storage controllers 125 a, 125 b asmulti-masters for both PCI buses 128 a, 128 b. Alternately, aPCI/NVMe/NVMf switching infrastructure or fabric may connect multiplestorage controllers. Some storage system embodiments may allow storagedevices to communicate with each other directly rather thancommunicating only with storage controllers. In one embodiment, astorage device controller 119 a may be operable under direction from astorage controller 125 a to synthesize and transfer data to be storedinto Flash memory devices from data that has been stored in RAM (e.g.,RAM 121 of FIG. 1C). For example, a recalculated version of RAM contentmay be transferred after a storage controller has determined that anoperation has fully committed across the storage system, or whenfast-write memory on the device has reached a certain used capacity, orafter a certain amount of time, to ensure improve safety of the data orto release addressable fast-write capacity for reuse. This mechanism maybe used, for example, to avoid a second transfer over a bus (e.g., 128a, 128 b) from the storage controllers 125 a, 125 b. In one embodiment,a recalculation may include compressing data, attaching indexing orother metadata, combining multiple data segments together, performingerasure code calculations, etc.

In one embodiment, under direction from a storage controller 125 a, 125b, a storage device controller 119 a, 119 b may be operable to calculateand transfer data to other storage devices from data stored in RAM(e.g., RAM 121 of FIG. 1C) without involvement of the storagecontrollers 125 a, 125 b. This operation may be used to mirror datastored in one storage controller 125 a to another storage controller 125b, or it could be used to offload compression, data aggregation, and/orerasure coding calculations and transfers to storage devices to reduceload on storage controllers or the storage controller interface 129 a,129 b to the PCI bus 128 a, 128 b.

A storage device controller 119A-D may include mechanisms forimplementing high availability primitives for use by other parts of astorage system external to the Dual PCI storage device 118. For example,reservation or exclusion primitives may be provided so that, in astorage system with two storage controllers providing a highly availablestorage service, one storage controller may prevent the other storagecontroller from accessing or continuing to access the storage device.This could be used, for example, in cases where one controller detectsthat the other controller is not functioning properly or where theinterconnect between the two storage controllers may itself not befunctioning properly.

In one embodiment, a storage system for use with Dual PCI direct mappedstorage devices with separately addressable fast write storage includessystems that manage erase blocks or groups of erase blocks as allocationunits for storing data on behalf of the storage service, or for storingmetadata (e.g., indexes, logs, etc.) associated with the storageservice, or for proper management of the storage system itself. Flashpages, which may be a few kilobytes in size, may be written as dataarrives or as the storage system is to persist data for long intervalsof time (e.g., above a defined threshold of time). To commit data morequickly, or to reduce the number of writes to the Flash memory devices,the storage controllers may first write data into the separatelyaddressable fast write storage on one more storage devices.

In one embodiment, the storage controllers 125 a, 125 b may initiate theuse of erase blocks within and across storage devices (e.g., 118) inaccordance with an age and expected remaining lifespan of the storagedevices, or based on other statistics. The storage controllers 125 a,125 b may initiate garbage collection and data migration data betweenstorage devices in accordance with pages that are no longer needed aswell as to manage Flash page and erase block lifespans and to manageoverall system performance.

In one embodiment, the storage system 124 may utilize mirroring and/orerasure coding schemes as part of storing data into addressable fastwrite storage and/or as part of writing data into allocation unitsassociated with erase blocks. Erasure codes may be used across storagedevices, as well as within erase blocks or allocation units, or withinand across Flash memory devices on a single storage device, to provideredundancy against single or multiple storage device failures or toprotect against internal corruptions of Flash memory pages resultingfrom Flash memory operations or from degradation of Flash memory cells.Mirroring and erasure coding at various levels may be used to recoverfrom multiple types of failures that occur separately or in combination.

The embodiments depicted with reference to FIGS. 2A-G illustrate astorage cluster that stores user data, such as user data originatingfrom one or more user or client systems or other sources external to thestorage cluster. The storage cluster distributes user data acrossstorage nodes housed within a chassis, or across multiple chassis, usingerasure coding and redundant copies of metadata. Erasure coding refersto a method of data protection or reconstruction in which data is storedacross a set of different locations, such as disks, storage nodes orgeographic locations. Flash memory is one type of solid-state memorythat may be integrated with the embodiments, although the embodimentsmay be extended to other types of solid-state memory or other storagemedium, including non-solid state memory. Control of storage locationsand workloads are distributed across the storage locations in aclustered peer-to-peer system. Tasks such as mediating communicationsbetween the various storage nodes, detecting when a storage node hasbecome unavailable, and balancing I/Os (inputs and outputs) across thevarious storage nodes, are all handled on a distributed basis. Data islaid out or distributed across multiple storage nodes in data fragmentsor stripes that support data recovery in some embodiments. Ownership ofdata can be reassigned within a cluster, independent of input and outputpatterns. This architecture described in more detail below allows astorage node in the cluster to fail, with the system remainingoperational, since the data can be reconstructed from other storagenodes and thus remain available for input and output operations. Invarious embodiments, a storage node may be referred to as a clusternode, a blade, or a server.

The storage cluster may be contained within a chassis, i.e., anenclosure housing one or more storage nodes. A mechanism to providepower to each storage node, such as a power distribution bus, and acommunication mechanism, such as a communication bus that enablescommunication between the storage nodes are included within the chassis.The storage cluster can run as an independent system in one locationaccording to some embodiments. In one embodiment, a chassis contains atleast two instances of both the power distribution and the communicationbus which may be enabled or disabled independently. The internalcommunication bus may be an Ethernet bus, however, other technologiessuch as PCIe, InfiniBand, and others, are equally suitable. The chassisprovides a port for an external communication bus for enablingcommunication between multiple chassis, directly or through a switch,and with client systems. The external communication may use a technologysuch as Ethernet, InfiniBand, Fibre Channel, etc. In some embodiments,the external communication bus uses different communication bustechnologies for inter-chassis and client communication. If a switch isdeployed within or between chassis, the switch may act as a translationbetween multiple protocols or technologies. When multiple chassis areconnected to define a storage cluster, the storage cluster may beaccessed by a client using either proprietary interfaces or standardinterfaces such as network file system (‘NFS’), common internet filesystem (‘CIFS’), small computer system interface (‘SCSI’) or hypertexttransfer protocol (‘HTTP’). Translation from the client protocol mayoccur at the switch, chassis external communication bus or within eachstorage node. In some embodiments, multiple chassis may be coupled orconnected to each other through an aggregator switch. A portion and/orall of the coupled or connected chassis may be designated as a storagecluster. As discussed above, each chassis can have multiple blades, eachblade has a media access control (‘MAC’) address, but the storagecluster is presented to an external network as having a single clusterIP address and a single MAC address in some embodiments.

Each storage node may be one or more storage servers and each storageserver is connected to one or more non-volatile solid state memoryunits, which may be referred to as storage units or storage devices. Oneembodiment includes a single storage server in each storage node andbetween one to eight non-volatile solid state memory units, however thisone example is not meant to be limiting. The storage server may includea processor, DRAM and interfaces for the internal communication bus andpower distribution for each of the power buses. Inside the storage node,the interfaces and storage unit share a communication bus, e.g., PCIExpress, in some embodiments. The non-volatile solid state memory unitsmay directly access the internal communication bus interface through astorage node communication bus, or request the storage node to accessthe bus interface. The non-volatile solid state memory unit contains anembedded CPU, solid state storage controller, and a quantity of solidstate mass storage, e.g., between 2-32 terabytes (‘TB’) in someembodiments. An embedded volatile storage medium, such as DRAM, and anenergy reserve apparatus are included in the non-volatile solid statememory unit. In some embodiments, the energy reserve apparatus is acapacitor, super-capacitor, or battery that enables transferring asubset of DRAM contents to a stable storage medium in the case of powerloss. In some embodiments, the non-volatile solid state memory unit isconstructed with a storage class memory, such as phase change ormagnetoresistive random access memory (‘MRAM’) that substitutes for DRAMand enables a reduced power hold-up apparatus.

One of many features of the storage nodes and non-volatile solid statestorage is the ability to proactively rebuild data in a storage cluster.The storage nodes and non-volatile solid state storage can determinewhen a storage node or non-volatile solid state storage in the storagecluster is unreachable, independent of whether there is an attempt toread data involving that storage node or non-volatile solid statestorage. The storage nodes and non-volatile solid state storage thencooperate to recover and rebuild the data in at least partially newlocations. This constitutes a proactive rebuild, in that the systemrebuilds data without waiting until the data is needed for a read accessinitiated from a client system employing the storage cluster. These andfurther details of the storage memory and operation thereof arediscussed below.

FIG. 2A is a perspective view of a storage cluster 161, with multiplestorage nodes 150 and internal solid-state memory coupled to eachstorage node to provide network attached storage or storage areanetwork, in accordance with some embodiments. A network attachedstorage, storage area network, or a storage cluster, or other storagememory, could include one or more storage clusters 161, each having oneor more storage nodes 150, in a flexible and reconfigurable arrangementof both the physical components and the amount of storage memoryprovided thereby. The storage cluster 161 is designed to fit in a rack,and one or more racks can be set up and populated as desired for thestorage memory. The storage cluster 161 has a chassis 138 havingmultiple slots 142. It should be appreciated that chassis 138 may bereferred to as a housing, enclosure, or rack unit. In one embodiment,the chassis 138 has fourteen slots 142, although other numbers of slotsare readily devised. For example, some embodiments have four slots,eight slots, sixteen slots, thirty-two slots, or other suitable numberof slots. Each slot 142 can accommodate one storage node 150 in someembodiments. Chassis 138 includes flaps 148 that can be utilized tomount the chassis 138 on a rack. Fans 144 provide air circulation forcooling of the storage nodes 150 and components thereof, although othercooling components could be used, or an embodiment could be devisedwithout cooling components. A switch fabric 146 couples storage nodes150 within chassis 138 together and to a network for communication tothe memory. In an embodiment depicted in herein, the slots 142 to theleft of the switch fabric 146 and fans 144 are shown occupied by storagenodes 150, while the slots 142 to the right of the switch fabric 146 andfans 144 are empty and available for insertion of storage node 150 forillustrative purposes. This configuration is one example, and one ormore storage nodes 150 could occupy the slots 142 in various furtherarrangements. The storage node arrangements need not be sequential oradjacent in some embodiments. Storage nodes 150 are hot pluggable,meaning that a storage node 150 can be inserted into a slot 142 in thechassis 138, or removed from a slot 142, without stopping or poweringdown the system. Upon insertion or removal of storage node 150 from slot142, the system automatically reconfigures in order to recognize andadapt to the change. Reconfiguration, in some embodiments, includesrestoring redundancy and/or rebalancing data or load.

Each storage node 150 can have multiple components. In the embodimentshown here, the storage node 150 includes a printed circuit board 159populated by a CPU 156, i.e., processor, a memory 154 coupled to the CPU156, and a non-volatile solid state storage 152 coupled to the CPU 156,although other mountings and/or components could be used in furtherembodiments. The memory 154 has instructions which are executed by theCPU 156 and/or data operated on by the CPU 156. As further explainedbelow, the non-volatile solid state storage 152 includes flash or, infurther embodiments, other types of solid-state memory.

Referring to FIG. 2A, storage cluster 161 is scalable, meaning thatstorage capacity with non-uniform storage sizes is readily added, asdescribed above. One or more storage nodes 150 can be plugged into orremoved from each chassis and the storage cluster self-configures insome embodiments. Plug-in storage nodes 150, whether installed in achassis as delivered or later added, can have different sizes. Forexample, in one embodiment a storage node 150 can have any multiple of 4TB, e.g., 8 TB, 12 TB, 16 TB, 32 TB, etc. In further embodiments, astorage node 150 could have any multiple of other storage amounts orcapacities. Storage capacity of each storage node 150 is broadcast, andinfluences decisions of how to stripe the data. For maximum storageefficiency, an embodiment can self-configure as wide as possible in thestripe, subject to a predetermined requirement of continued operationwith loss of up to one, or up to two, non-volatile solid state storage152 units or storage nodes 150 within the chassis.

FIG. 2B is a block diagram showing a communications interconnect 173 andpower distribution bus 172 coupling multiple storage nodes 150.Referring back to FIG. 2A, the communications interconnect 173 can beincluded in or implemented with the switch fabric 146 in someembodiments. Where multiple storage clusters 161 occupy a rack, thecommunications interconnect 173 can be included in or implemented with atop of rack switch, in some embodiments. As illustrated in FIG. 2B,storage cluster 161 is enclosed within a single chassis 138. Externalport 176 is coupled to storage nodes 150 through communicationsinterconnect 173, while external port 174 is coupled directly to astorage node. External power port 178 is coupled to power distributionbus 172. Storage nodes 150 may include varying amounts and differingcapacities of non-volatile solid state storage 152 as described withreference to FIG. 2A. In addition, one or more storage nodes 150 may bea compute only storage node as illustrated in FIG. 2B. Authorities 168are implemented on the non-volatile solid state storage 152, for exampleas lists or other data structures stored in memory. In some embodimentsthe authorities are stored within the non-volatile solid state storage152 and supported by software executing on a controller or otherprocessor of the non-volatile solid state storage 152. In a furtherembodiment, authorities 168 are implemented on the storage nodes 150,for example as lists or other data structures stored in the memory 154and supported by software executing on the CPU 156 of the storage node150. Authorities 168 control how and where data is stored in thenon-volatile solid state storage 152 in some embodiments. This controlassists in determining which type of erasure coding scheme is applied tothe data, and which storage nodes 150 have which portions of the data.Each authority 168 may be assigned to a non-volatile solid state storage152. Each authority may control a range of inode numbers, segmentnumbers, or other data identifiers which are assigned to data by a filesystem, by the storage nodes 150, or by the non-volatile solid statestorage 152, in various embodiments.

Every piece of data, and every piece of metadata, has redundancy in thesystem in some embodiments. In addition, every piece of data and everypiece of metadata has an owner, which may be referred to as anauthority. If that authority is unreachable, for example through failureof a storage node, there is a plan of succession for how to find thatdata or that metadata. In various embodiments, there are redundantcopies of authorities 168. Authorities 168 have a relationship tostorage nodes 150 and non-volatile solid state storage 152 in someembodiments. Each authority 168, covering a range of data segmentnumbers or other identifiers of the data, may be assigned to a specificnon-volatile solid state storage 152. In some embodiments theauthorities 168 for all of such ranges are distributed over thenon-volatile solid state storage 152 of a storage cluster. Each storagenode 150 has a network port that provides access to the non-volatilesolid state storage(s) 152 of that storage node 150. Data can be storedin a segment, which is associated with a segment number and that segmentnumber is an indirection for a configuration of a RAID (redundant arrayof independent disks) stripe in some embodiments. The assignment and useof the authorities 168 thus establishes an indirection to data.Indirection may be referred to as the ability to reference dataindirectly, in this case via an authority 168, in accordance with someembodiments. A segment identifies a set of non-volatile solid statestorage 152 and a local identifier into the set of non-volatile solidstate storage 152 that may contain data. In some embodiments, the localidentifier is an offset into the device and may be reused sequentiallyby multiple segments. In other embodiments the local identifier isunique for a specific segment and never reused. The offsets in thenon-volatile solid state storage 152 are applied to locating data forwriting to or reading from the non-volatile solid state storage 152 (inthe form of a RAID stripe). Data is striped across multiple units ofnon-volatile solid state storage 152, which may include or be differentfrom the non-volatile solid state storage 152 having the authority 168for a particular data segment.

If there is a change in where a particular segment of data is located,e.g., during a data move or a data reconstruction, the authority 168 forthat data segment should be consulted, at that non-volatile solid statestorage 152 or storage node 150 having that authority 168. In order tolocate a particular piece of data, embodiments calculate a hash valuefor a data segment or apply an inode number or a data segment number.The output of this operation points to a non-volatile solid statestorage 152 having the authority 168 for that particular piece of data.In some embodiments there are two stages to this operation. The firststage maps an entity identifier (ID), e.g., a segment number, inodenumber, or directory number to an authority identifier. This mapping mayinclude a calculation such as a hash or a bit mask. The second stage ismapping the authority identifier to a particular non-volatile solidstate storage 152, which may be done through an explicit mapping. Theoperation is repeatable, so that when the calculation is performed, theresult of the calculation repeatably and reliably points to a particularnon-volatile solid state storage 152 having that authority 168. Theoperation may include the set of reachable storage nodes as input. Ifthe set of reachable non-volatile solid state storage units changes theoptimal set changes. In some embodiments, the persisted value is thecurrent assignment (which is always true) and the calculated value isthe target assignment the cluster will attempt to reconfigure towards.This calculation may be used to determine the optimal non-volatile solidstate storage 152 for an authority in the presence of a set ofnon-volatile solid state storage 152 that are reachable and constitutethe same cluster. The calculation also determines an ordered set of peernon-volatile solid state storage 152 that will also record the authorityto non-volatile solid state storage mapping so that the authority may bedetermined even if the assigned non-volatile solid state storage isunreachable. A duplicate or substitute authority 168 may be consulted ifa specific authority 168 is unavailable in some embodiments.

With reference to FIG. 2A and 2B, two of the many tasks of the CPU 156on a storage node 150 are to break up write data, and reassemble readdata. When the system has determined that data is to be written, theauthority 168 for that data is located as above. When the segment ID fordata is already determined the request to write is forwarded to thenon-volatile solid state storage 152 currently determined to be the hostof the authority 168 determined from the segment. The host CPU 156 ofthe storage node 150, on which the non-volatile solid state storage 152and corresponding authority 168 reside, then breaks up or shards thedata and transmits the data out to various non-volatile solid statestorage 152. The transmitted data is written as a data stripe inaccordance with an erasure coding scheme. In some embodiments, data isrequested to be pulled, and in other embodiments, data is pushed. Inreverse, when data is read, the authority 168 for the segment IDcontaining the data is located as described above. The host CPU 156 ofthe storage node 150 on which the non-volatile solid state storage 152and corresponding authority 168 reside requests the data from thenon-volatile solid state storage and corresponding storage nodes pointedto by the authority. In some embodiments the data is read from flashstorage as a data stripe. The host CPU 156 of storage node 150 thenreassembles the read data, correcting any errors (if present) accordingto the appropriate erasure coding scheme, and forwards the reassembleddata to the network. In further embodiments, some or all of these taskscan be handled in the non-volatile solid state storage 152. In someembodiments, the segment host requests the data be sent to storage node150 by requesting pages from storage and then sending the data to thestorage node making the original request.

In embodiments, authorities 168 operate to determine how operations willproceed against particular logical elements. Each of the logicalelements may be operated on through a particular authority across aplurality of storage controllers of a storage system. The authorities168 may communicate with the plurality of storage controllers so thatthe plurality of storage controllers collectively perform operationsagainst those particular logical elements.

In embodiments, logical elements could be, for example, files,directories, object buckets, individual objects, delineated parts offiles or objects, other forms of key-value pair databases, or tables. Inembodiments, performing an operation can involve, for example, ensuringconsistency, structural integrity, and/or recoverability with otheroperations against the same logical element, reading metadata and dataassociated with that logical element, determining what data should bewritten durably into the storage system to persist any changes for theoperation, or where metadata and data can be determined to be storedacross modular storage devices attached to a plurality of the storagecontrollers in the storage system.

In some embodiments the operations are token based transactions toefficiently communicate within a distributed system. Each transactionmay be accompanied by or associated with a token, which gives permissionto execute the transaction. The authorities 168 are able to maintain apre-transaction state of the system until completion of the operation insome embodiments. The token based communication may be accomplishedwithout a global lock across the system, and also enables restart of anoperation in case of a disruption or other failure.

In some systems, for example in UNIX-style file systems, data is handledwith an index node or inode, which specifies a data structure thatrepresents an object in a file system. The object could be a file or adirectory, for example. Metadata may accompany the object, as attributessuch as permission data and a creation timestamp, among otherattributes. A segment number could be assigned to all or a portion ofsuch an object in a file system. In other systems, data segments arehandled with a segment number assigned elsewhere. For purposes ofdiscussion, the unit of distribution is an entity, and an entity can bea file, a directory or a segment. That is, entities are units of data ormetadata stored by a storage system. Entities are grouped into setscalled authorities. Each authority has an authority owner, which is astorage node that has the exclusive right to update the entities in theauthority. In other words, a storage node contains the authority, andthat the authority, in turn, contains entities.

A segment is a logical container of data in accordance with someembodiments. A segment is an address space between medium address spaceand physical flash locations, i.e., the data segment number, are in thisaddress space. Segments may also contain meta-data, which enable dataredundancy to be restored (rewritten to different flash locations ordevices) without the involvement of higher level software. In oneembodiment, an internal format of a segment contains client data andmedium mappings to determine the position of that data. Each datasegment is protected, e.g., from memory and other failures, by breakingthe segment into a number of data and parity shards, where applicable.The data and parity shards are distributed, i.e., striped, acrossnon-volatile solid state storage 152 coupled to the host CPUs 156 (SeeFIGS. 2E and 2G) in accordance with an erasure coding scheme. Usage ofthe term segments refers to the container and its place in the addressspace of segments in some embodiments. Usage of the term stripe refersto the same set of shards as a segment and includes how the shards aredistributed along with redundancy or parity information in accordancewith some embodiments.

A series of address-space transformations takes place across an entirestorage system. At the top are the directory entries (file names) whichlink to an inode. Modes point into medium address space, where data islogically stored. Medium addresses may be mapped through a series ofindirect mediums to spread the load of large files, or implement dataservices like deduplication or snapshots. Medium addresses may be mappedthrough a series of indirect mediums to spread the load of large files,or implement data services like deduplication or snapshots. Segmentaddresses are then translated into physical flash locations. Physicalflash locations have an address range bounded by the amount of flash inthe system in accordance with some embodiments. Medium addresses andsegment addresses are logical containers, and in some embodiments use a128 bit or larger identifier so as to be practically infinite, with alikelihood of reuse calculated as longer than the expected life of thesystem. Addresses from logical containers are allocated in ahierarchical fashion in some embodiments. Initially, each non-volatilesolid state storage 152 unit may be assigned a range of address space.Within this assigned range, the non-volatile solid state storage 152 isable to allocate addresses without synchronization with othernon-volatile solid state storage 152.

Data and metadata is stored by a set of underlying storage layouts thatare optimized for varying workload patterns and storage devices. Theselayouts incorporate multiple redundancy schemes, compression formats andindex algorithms. Some of these layouts store information aboutauthorities and authority masters, while others store file metadata andfile data. The redundancy schemes include error correction codes thattolerate corrupted bits within a single storage device (such as a NANDflash chip), erasure codes that tolerate the failure of multiple storagenodes, and replication schemes that tolerate data center or regionalfailures. In some embodiments, low density parity check (‘LDPC’) code isused within a single storage unit. Reed-Solomon encoding is used withina storage cluster, and mirroring is used within a storage grid in someembodiments. Metadata may be stored using an ordered log structuredindex (such as a Log Structured Merge Tree), and large data may not bestored in a log structured layout.

In order to maintain consistency across multiple copies of an entity,the storage nodes agree implicitly on two things through calculations:(1) the authority that contains the entity, and (2) the storage nodethat contains the authority. The assignment of entities to authoritiescan be done by pseudo randomly assigning entities to authorities, bysplitting entities into ranges based upon an externally produced key, orby placing a single entity into each authority. Examples of pseudorandomschemes are linear hashing and the Replication Under Scalable Hashing(‘RUSH’) family of hashes, including Controlled Replication UnderScalable Hashing (‘CRUSH’). In some embodiments, pseudo-randomassignment is utilized only for assigning authorities to nodes becausethe set of nodes can change. The set of authorities cannot change so anysubjective function may be applied in these embodiments. Some placementschemes automatically place authorities on storage nodes, while otherplacement schemes rely on an explicit mapping of authorities to storagenodes. In some embodiments, a pseudorandom scheme is utilized to mapfrom each authority to a set of candidate authority owners. Apseudorandom data distribution function related to CRUSH may assignauthorities to storage nodes and create a list of where the authoritiesare assigned. Each storage node has a copy of the pseudorandom datadistribution function, and can arrive at the same calculation fordistributing, and later finding or locating an authority. Each of thepseudorandom schemes requires the reachable set of storage nodes asinput in some embodiments in order to conclude the same target nodes.Once an entity has been placed in an authority, the entity may be storedon physical devices so that no expected failure will lead to unexpecteddata loss. In some embodiments, rebalancing algorithms attempt to storethe copies of all entities within an authority in the same layout and onthe same set of machines.

Examples of expected failures include device failures, stolen machines,datacenter fires, and regional disasters, such as nuclear or geologicalevents. Different failures lead to different levels of acceptable dataloss. In some embodiments, a stolen storage node impacts neither thesecurity nor the reliability of the system, while depending on systemconfiguration, a regional event could lead to no loss of data, a fewseconds or minutes of lost updates, or even complete data loss.

In the embodiments, the placement of data for storage redundancy isindependent of the placement of authorities for data consistency. Insome embodiments, storage nodes that contain authorities do not containany persistent storage. Instead, the storage nodes are connected tonon-volatile solid state storage units that do not contain authorities.The communications interconnect between storage nodes and non-volatilesolid state storage units consists of multiple communicationtechnologies and has non-uniform performance and fault tolerancecharacteristics. In some embodiments, as mentioned above, non-volatilesolid state storage units are connected to storage nodes via PCIexpress, storage nodes are connected together within a single chassisusing Ethernet backplane, and chassis are connected together to form astorage cluster. Storage clusters are connected to clients usingEthernet or fiber channel in some embodiments. If multiple storageclusters are configured into a storage grid, the multiple storageclusters are connected using the Internet or other long-distancenetworking links, such as a “metro scale” link or private link that doesnot traverse the internet.

Authority owners have the exclusive right to modify entities, to migrateentities from one non-volatile solid state storage unit to anothernon-volatile solid state storage unit, and to add and remove copies ofentities. This allows for maintaining the redundancy of the underlyingdata. When an authority owner fails, is going to be decommissioned, oris overloaded, the authority is transferred to a new storage node.Transient failures make it non-trivial to ensure that all non-faultymachines agree upon the new authority location. The ambiguity thatarises due to transient failures can be achieved automatically by aconsensus protocol such as Paxos, hot-warm failover schemes, via manualintervention by a remote system administrator, or by a local hardwareadministrator (such as by physically removing the failed machine fromthe cluster, or pressing a button on the failed machine). In someembodiments, a consensus protocol is used, and failover is automatic. Iftoo many failures or replication events occur in too short a timeperiod, the system goes into a self-preservation mode and haltsreplication and data movement activities until an administratorintervenes in accordance with some embodiments.

As authorities are transferred between storage nodes and authorityowners update entities in their authorities, the system transfersmessages between the storage nodes and non-volatile solid state storageunits. With regard to persistent messages, messages that have differentpurposes are of different types. Depending on the type of the message,the system maintains different ordering and durability guarantees. Asthe persistent messages are being processed, the messages aretemporarily stored in multiple durable and non-durable storage hardwaretechnologies. In some embodiments, messages are stored in RAM, NVRAM andon NAND flash devices, and a variety of protocols are used in order tomake efficient use of each storage medium. Latency-sensitive clientrequests may be persisted in replicated NVRAM, and then later NAND,while background rebalancing operations are persisted directly to NAND.

Persistent messages are persistently stored prior to being transmitted.This allows the system to continue to serve client requests despitefailures and component replacement. Although many hardware componentscontain unique identifiers that are visible to system administrators,manufacturer, hardware supply chain and ongoing monitoring qualitycontrol infrastructure, applications running on top of theinfrastructure address virtualize addresses. These virtualized addressesdo not change over the lifetime of the storage system, regardless ofcomponent failures and replacements. This allows each component of thestorage system to be replaced over time without reconfiguration ordisruptions of client request processing, i.e., the system supportsnon-disruptive upgrades.

In some embodiments, the virtualized addresses are stored withsufficient redundancy. A continuous monitoring system correlateshardware and software status and the hardware identifiers. This allowsdetection and prediction of failures due to faulty components andmanufacturing details. The monitoring system also enables the proactivetransfer of authorities and entities away from impacted devices beforefailure occurs by removing the component from the critical path in someembodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode 150 and contents of a non-volatile solid state storage 152 of thestorage node 150. Data is communicated to and from the storage node 150by a network interface controller (‘NIC’) 202 in some embodiments. Eachstorage node 150 has a CPU 156, and one or more non-volatile solid statestorage 152, as discussed above. Moving down one level in FIG. 2C, eachnon-volatile solid state storage 152 has a relatively fast non-volatilesolid state memory, such as nonvolatile random access memory (‘NVRAM’)204, and flash memory 206. In some embodiments, NVRAM 204 may be acomponent that does not require program/erase cycles (DRAM, MRAM, PCM),and can be a memory that can support being written vastly more oftenthan the memory is read from. Moving down another level in FIG. 2C, theNVRAM 204 is implemented in one embodiment as high speed volatilememory, such as dynamic random access memory (DRAM) 216, backed up byenergy reserve 218. Energy reserve 218 provides sufficient electricalpower to keep the DRAM 216 powered long enough for contents to betransferred to the flash memory 206 in the event of power failure. Insome embodiments, energy reserve 218 is a capacitor, super-capacitor,battery, or other device, that supplies a suitable supply of energysufficient to enable the transfer of the contents of DRAM 216 to astable storage medium in the case of power loss. The flash memory 206 isimplemented as multiple flash dies 222, which may be referred to aspackages of flash dies 222 or an array of flash dies 222. It should beappreciated that the flash dies 222 could be packaged in any number ofways, with a single die per package, multiple dies per package (i.e.,multichip packages), in hybrid packages, as bare dies on a printedcircuit board or other substrate, as encapsulated dies, etc. In theembodiment shown, the non-volatile solid state storage 152 has acontroller 212 or other processor, and an input output (I/O) port 210coupled to the controller 212. I/O port 210 is coupled to the CPU 156and/or the network interface controller 202 of the flash storage node150. Flash input output (I/O) port 220 is coupled to the flash dies 222,and a direct memory access unit (DMA) 214 is coupled to the controller212, the DRAM 216 and the flash dies 222. In the embodiment shown, theI/O port 210, controller 212, DMA unit 214 and flash I/O port 220 areimplemented on a programmable logic device (‘PLD’) 208, e.g., an FPGA.In this embodiment, each flash die 222 has pages, organized as sixteenkB (kilobyte) pages 224, and a register 226 through which data can bewritten to or read from the flash die 222. In further embodiments, othertypes of solid-state memory are used in place of, or in addition toflash memory illustrated within flash die 222.

Storage clusters 161, in various embodiments as disclosed herein, can becontrasted with storage arrays in general. The storage nodes 150 arepart of a collection that creates the storage cluster 161. Each storagenode 150 owns a slice of data and computing required to provide thedata. Multiple storage nodes 150 cooperate to store and retrieve thedata. Storage memory or storage devices, as used in storage arrays ingeneral, are less involved with processing and manipulating the data.Storage memory or storage devices in a storage array receive commands toread, write, or erase data. The storage memory or storage devices in astorage array are not aware of a larger system in which they areembedded, or what the data means. Storage memory or storage devices instorage arrays can include various types of storage memory, such as RAM,solid state drives, hard disk drives, etc. The non-volatile solid statestorage 152 units described herein have multiple interfaces activesimultaneously and serving multiple purposes. In some embodiments, someof the functionality of a storage node 150 is shifted into a storageunit 152, transforming the storage unit 152 into a combination ofstorage unit 152 and storage node 150. Placing computing (relative tostorage data) into the storage unit 152 places this computing closer tothe data itself. The various system embodiments have a hierarchy ofstorage node layers with different capabilities. By contrast, in astorage array, a controller owns and knows everything about all of thedata that the controller manages in a shelf or storage devices. In astorage cluster 161, as described herein, multiple controllers inmultiple non-volatile sold state storage 152 units and/or storage nodes150 cooperate in various ways (e.g., for erasure coding, data sharding,metadata communication and redundancy, storage capacity expansion orcontraction, data recovery, and so on).

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes 150 and storage 152 units of FIGS. 2A-C. In thisversion, each non-volatile solid state storage 152 unit has a processorsuch as controller 212 (see FIG. 2C), an FPGA, flash memory 206, andNVRAM 204 (which is super-capacitor backed DRAM 216, see FIGS. 2B and2C) on a PCIe (peripheral component interconnect express) board in achassis 138 (see FIG. 2A). The non-volatile solid state storage 152 unitmay be implemented as a single board containing storage, and may be thelargest tolerable failure domain inside the chassis. In someembodiments, up to two non-volatile solid state storage 152 units mayfail and the device will continue with no data loss.

The physical storage is divided into named regions based on applicationusage in some embodiments. The NVRAM 204 is a contiguous block ofreserved memory in the non-volatile solid state storage 152 DRAM 216,and is backed by NAND flash. NVRAM 204 is logically divided intomultiple memory regions written for two as spool (e.g., spool_region).Space within the NVRAM 204 spools is managed by each authority 168independently. Each device provides an amount of storage space to eachauthority 168. That authority 168 further manages lifetimes andallocations within that space. Examples of a spool include distributedtransactions or notions. When the primary power to a non-volatile solidstate storage 152 unit fails, onboard super-capacitors provide a shortduration of power hold up. During this holdup interval, the contents ofthe NVRAM 204 are flushed to flash memory 206. On the next power-on, thecontents of the NVRAM 204 are recovered from the flash memory 206.

As for the storage unit controller, the responsibility of the logical“controller” is distributed across each of the blades containingauthorities 168. This distribution of logical control is shown in FIG.2D as a host controller 242, mid-tier controller 244 and storage unitcontroller(s) 246. Management of the control plane and the storage planeare treated independently, although parts may be physically co-locatedon the same blade. Each authority 168 effectively serves as anindependent controller. Each authority 168 provides its own data andmetadata structures, its own background workers, and maintains its ownlifecycle.

FIG. 2E is a blade 252 hardware block diagram, showing a control plane254, compute and storage planes 256, 258, and authorities 168interacting with underlying physical resources, using embodiments of thestorage nodes 150 and storage units 152 of FIGS. 2A-C in the storageserver environment of FIG. 2D. The control plane 254 is partitioned intoa number of authorities 168 which can use the compute resources in thecompute plane 256 to run on any of the blades 252. The storage plane 258is partitioned into a set of devices, each of which provides access toflash 206 and NVRAM 204 resources. In one embodiment, the compute plane256 may perform the operations of a storage array controller, asdescribed herein, on one or more devices of the storage plane 258 (e.g.,a storage array).

In the compute and storage planes 256, 258 of FIG. 2E, the authorities168 interact with the underlying physical resources (i.e., devices).From the point of view of an authority 168, its resources are stripedover all of the physical devices. From the point of view of a device, itprovides resources to all authorities 168, irrespective of where theauthorities happen to run. Each authority 168 has allocated or has beenallocated one or more partitions 260 of storage memory in the storageunits 152, e.g., partitions 260 in flash memory 206 and NVRAM 204. Eachauthority 168 uses those allocated partitions 260 that belong to it, forwriting or reading user data. Authorities can be associated withdiffering amounts of physical storage of the system. For example, oneauthority 168 could have a larger number of partitions 260 or largersized partitions 260 in one or more storage units 152 than one or moreother authorities 168.

FIG. 2F depicts elasticity software layers in blades 252 of a storagecluster, in accordance with some embodiments. In the elasticitystructure, elasticity software is symmetric, i.e., each blade's computemodule 270 runs the three identical layers of processes depicted in FIG.2F. Storage managers 274 execute read and write requests from otherblades 252 for data and metadata stored in local storage unit 152 NVRAM204 and flash 206. Authorities 168 fulfill client requests by issuingthe necessary reads and writes to the blades 252 on whose storage units152 the corresponding data or metadata resides. Endpoints 272 parseclient connection requests received from switch fabric 146 supervisorysoftware, relay the client connection requests to the authorities 168responsible for fulfillment, and relay the authorities' 168 responses toclients. The symmetric three-layer structure enables the storagesystem's high degree of concurrency. Elasticity scales out efficientlyand reliably in these embodiments. In addition, elasticity implements aunique scale-out technique that balances work evenly across allresources regardless of client access pattern, and maximizes concurrencyby eliminating much of the need for inter-blade coordination thattypically occurs with conventional distributed locking.

Still referring to FIG. 2F, authorities 168 running in the computemodules 270 of a blade 252 perform the internal operations required tofulfill client requests. One feature of elasticity is that authorities168 are stateless, i.e., they cache active data and metadata in theirown blades' 252 DRAMs for fast access, but the authorities store everyupdate in their NVRAM 204 partitions on three separate blades 252 untilthe update has been written to flash 206. All the storage system writesto NVRAM 204 are in triplicate to partitions on three separate blades252 in some embodiments. With triple-mirrored NVRAM 204 and persistentstorage protected by parity and Reed-Solomon RAID checksums, the storagesystem can survive concurrent failure of two blades 252 with no loss ofdata, metadata, or access to either.

Because authorities 168 are stateless, they can migrate between blades252. Each authority 168 has a unique identifier. NVRAM 204 and flash 206partitions are associated with authorities' 168 identifiers, not withthe blades 252 on which they are running in some. Thus, when anauthority 168 migrates, the authority 168 continues to manage the samestorage partitions from its new location. When a new blade 252 isinstalled in an embodiment of the storage cluster, the systemautomatically rebalances load by: partitioning the new blade's 252storage for use by the system's authorities 168, migrating selectedauthorities 168 to the new blade 252, starting endpoints 272 on the newblade 252 and including them in the switch fabric's 146 clientconnection distribution algorithm.

From their new locations, migrated authorities 168 persist the contentsof their NVRAM 204 partitions on flash 206, process read and writerequests from other authorities 168, and fulfill the client requeststhat endpoints 272 direct to them. Similarly, if a blade 252 fails or isremoved, the system redistributes its authorities 168 among the system'sremaining blades 252. The redistributed authorities 168 continue toperform their original functions from their new locations.

FIG. 2G depicts authorities 168 and storage resources in blades 252 of astorage cluster, in accordance with some embodiments. Each authority 168is exclusively responsible for a partition of the flash 206 and NVRAM204 on each blade 252. The authority 168 manages the content andintegrity of its partitions independently of other authorities 168.Authorities 168 compress incoming data and preserve it temporarily intheir NVRAM 204 partitions, and then consolidate, RAID-protect, andpersist the data in segments of the storage in their flash 206partitions. As the authorities 168 write data to flash 206, storagemanagers 274 perform the necessary flash translation to optimize writeperformance and maximize media longevity. In the background, authorities168 “garbage collect,” or reclaim space occupied by data that clientshave made obsolete by overwriting the data. It should be appreciatedthat since authorities' 168 partitions are disjoint, there is no needfor distributed locking to execute client and writes or to performbackground functions.

The embodiments described herein may utilize various software,communication and/or networking protocols. In addition, theconfiguration of the hardware and/or software may be adjusted toaccommodate various protocols. For example, the embodiments may utilizeActive Directory, which is a database based system that providesauthentication, directory, policy, and other services in a WINDOWS™environment. In these embodiments, LDAP (Lightweight Directory AccessProtocol) is one example application protocol for querying and modifyingitems in directory service providers such as Active Directory. In someembodiments, a network lock manager (‘NLM’) is utilized as a facilitythat works in cooperation with the Network File System (‘NFS’) toprovide a System V style of advisory file and record locking over anetwork. The Server Message Block (‘SMB’) protocol, one version of whichis also known as Common Internet File System (‘CIFS’), may be integratedwith the storage systems discussed herein. SMP operates as anapplication-layer network protocol typically used for providing sharedaccess to files, printers, and serial ports and miscellaneouscommunications between nodes on a network. SMB also provides anauthenticated inter-process communication mechanism. AMAZON™ S3 (SimpleStorage Service) is a web service offered by Amazon Web Services, andthe systems described herein may interface with Amazon S3 through webservices interfaces (REST (representational state transfer), SOAP(simple object access protocol), and BitTorrent). A RESTful API(application programming interface) breaks down a transaction to createa series of small modules. Each module addresses a particular underlyingpart of the transaction. The control or permissions provided with theseembodiments, especially for object data, may include utilization of anaccess control list (‘ACL’). The ACL is a list of permissions attachedto an object and the ACL specifies which users or system processes aregranted access to objects, as well as what operations are allowed ongiven objects. The systems may utilize Internet Protocol version 6(‘IPv6’), as well as IPv4, for the communications protocol that providesan identification and location system for computers on networks androutes traffic across the Internet. The routing of packets betweennetworked systems may include Equal-cost multi-path routing (‘ECMP’),which is a routing strategy where next-hop packet forwarding to a singledestination can occur over multiple “best paths” which tie for top placein routing metric calculations. Multi-path routing can be used inconjunction with most routing protocols, because it is a per-hopdecision limited to a single router. The software may supportMulti-tenancy, which is an architecture in which a single instance of asoftware application serves multiple customers. Each customer may bereferred to as a tenant. Tenants may be given the ability to customizesome parts of the application, but may not customize the application'scode, in some embodiments. The embodiments may maintain audit logs. Anaudit log is a document that records an event in a computing system. Inaddition to documenting what resources were accessed, audit log entriestypically include destination and source addresses, a timestamp, anduser login information for compliance with various regulations. Theembodiments may support various key management policies, such asencryption key rotation. In addition, the system may support dynamicroot passwords or some variation dynamically changing passwords.

FIG. 3A sets forth a diagram of a storage system 306 that is coupled fordata communications with a cloud services provider 302 in accordancewith some embodiments of the present disclosure. Although depicted inless detail, the storage system 306 depicted in FIG. 3A may be similarto the storage systems described above with reference to FIGS. 1A-1D andFIGS. 2A-2G. In some embodiments, the storage system 306 depicted inFIG. 3A may be embodied as a storage system that includes imbalancedactive/active controllers, as a storage system that includes balancedactive/active controllers, as a storage system that includesactive/active controllers where less than all of each controller'sresources are utilized such that each controller has reserve resourcesthat may be used to support failover, as a storage system that includesfully active/active controllers, as a storage system that includesdataset-segregated controllers, as a storage system that includesdual-layer architectures with front-end controllers and back-endintegrated storage controllers, as a storage system that includesscale-out clusters of dual-controller arrays, as well as combinations ofsuch embodiments.

In the example depicted in FIG. 3A, the storage system 306 is coupled tothe cloud services provider 302 via a data communications link 304. Thedata communications link 304 may be embodied as a dedicated datacommunications link, as a data communications pathway that is providedthrough the use of one or data communications networks such as a widearea network (‘WAN’) or LAN, or as some other mechanism capable oftransporting digital information between the storage system 306 and thecloud services provider 302. Such a data communications link 304 may befully wired, fully wireless, or some aggregation of wired and wirelessdata communications pathways. In such an example, digital informationmay be exchanged between the storage system 306 and the cloud servicesprovider 302 via the data communications link 304 using one or more datacommunications protocols. For example, digital information may beexchanged between the storage system 306 and the cloud services provider302 via the data communications link 304 using the handheld devicetransfer protocol (‘HDTP’), hypertext transfer protocol (‘HTTP’),internet protocol (‘IP’), real-time transfer protocol (‘RTP’),transmission control protocol (‘TCP’), user datagram protocol (‘UDP’),wireless application protocol (‘WAP’), or other protocol.

The cloud services provider 302 depicted in FIG. 3A may be embodied, forexample, as a system and computing environment that provides a vastarray of services to users of the cloud services provider 302 throughthe sharing of computing resources via the data communications link 304.The cloud services provider 302 may provide on-demand access to a sharedpool of configurable computing resources such as computer networks,servers, storage, applications and services, and so on. The shared poolof configurable resources may be rapidly provisioned and released to auser of the cloud services provider 302 with minimal management effort.Generally, the user of the cloud services provider 302 is unaware of theexact computing resources utilized by the cloud services provider 302 toprovide the services. Although in many cases such a cloud servicesprovider 302 may be accessible via the Internet, readers of skill in theart will recognize that any system that abstracts the use of sharedresources to provide services to a user through any data communicationslink may be considered a cloud services provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe configured to provide a variety of services to the storage system 306and users of the storage system 306 through the implementation ofvarious service models. For example, the cloud services provider 302 maybe configured to provide services through the implementation of aninfrastructure as a service (‘IaaS’) service model, through theimplementation of a platform as a service (‘PaaS’) service model,through the implementation of a software as a service (‘SaaS’) servicemodel, through the implementation of an authentication as a service(‘AaaS’) service model, through the implementation of a storage as aservice model where the cloud services provider 302 offers access to itsstorage infrastructure for use by the storage system 306 and users ofthe storage system 306, and so on. Readers will appreciate that thecloud services provider 302 may be configured to provide additionalservices to the storage system 306 and users of the storage system 306through the implementation of additional service models, as the servicemodels described above are included only for explanatory purposes and inno way represent a limitation of the services that may be offered by thecloud services provider 302 or a limitation as to the service modelsthat may be implemented by the cloud services provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe embodied, for example, as a private cloud, as a public cloud, or as acombination of a private cloud and public cloud. In an embodiment inwhich the cloud services provider 302 is embodied as a private cloud,the cloud services provider 302 may be dedicated to providing servicesto a single organization rather than providing services to multipleorganizations. In an embodiment where the cloud services provider 302 isembodied as a public cloud, the cloud services provider 302 may provideservices to multiple organizations. In still alternative embodiments,the cloud services provider 302 may be embodied as a mix of a privateand public cloud services with a hybrid cloud deployment.

Although not explicitly depicted in FIG. 3A, readers will appreciatethat a vast amount of additional hardware components and additionalsoftware components may be necessary to facilitate the delivery of cloudservices to the storage system 306 and users of the storage system 306.For example, the storage system 306 may be coupled to (or even include)a cloud storage gateway. Such a cloud storage gateway may be embodied,for example, as hardware-based or software-based appliance that islocated on premise with the storage system 306. Such a cloud storagegateway may operate as a bridge between local applications that areexecuting on the storage system 306 and remote, cloud-based storage thatis utilized by the storage system 306. Through the use of a cloudstorage gateway, organizations may move primary iSCSI or NAS to thecloud services provider 302, thereby enabling the organization to savespace on their on-premises storage systems. Such a cloud storage gatewaymay be configured to emulate a disk array, a block-based device, a fileserver, or other storage system that can translate the SCSI commands,file server commands, or other appropriate command into REST-spaceprotocols that facilitate communications with the cloud servicesprovider 302.

In order to enable the storage system 306 and users of the storagesystem 306 to make use of the services provided by the cloud servicesprovider 302, a cloud migration process may take place during whichdata, applications, or other elements from an organization's localsystems (or even from another cloud environment) are moved to the cloudservices provider 302. In order to successfully migrate data,applications, or other elements to the cloud services provider's 302environment, middleware such as a cloud migration tool may be utilizedto bridge gaps between the cloud services provider's 302 environment andan organization's environment. Such cloud migration tools may also beconfigured to address potentially high network costs and long transfertimes associated with migrating large volumes of data to the cloudservices provider 302, as well as addressing security concernsassociated with sensitive data to the cloud services provider 302 overdata communications networks. In order to further enable the storagesystem 306 and users of the storage system 306 to make use of theservices provided by the cloud services provider 302, a cloudorchestrator may also be used to arrange and coordinate automated tasksin pursuit of creating a consolidated process or workflow. Such a cloudorchestrator may perform tasks such as configuring various components,whether those components are cloud components or on-premises components,as well as managing the interconnections between such components. Thecloud orchestrator can simplify the inter-component communication andconnections to ensure that links are correctly configured andmaintained.

In the example depicted in FIG. 3A, and as described briefly above, thecloud services provider 302 may be configured to provide services to thestorage system 306 and users of the storage system 306 through the usageof a SaaS service model, eliminating the need to install and run theapplication on local computers, which may simplify maintenance andsupport of the application. Such applications may take many forms inaccordance with various embodiments of the present disclosure. Forexample, the cloud services provider 302 may be configured to provideaccess to data analytics applications to the storage system 306 andusers of the storage system 306. Such data analytics applications may beconfigured, for example, to receive vast amounts of telemetry dataphoned home by the storage system 306. Such telemetry data may describevarious operating characteristics of the storage system 306 and may beanalyzed for a vast array of purposes including, for example, todetermine the health of the storage system 306, to identify workloadsthat are executing on the storage system 306, to predict when thestorage system 306 will run out of various resources, to recommendconfiguration changes, hardware or software upgrades, workflowmigrations, or other actions that may improve the operation of thestorage system 306.

The cloud services provider 302 may also be configured to provide accessto virtualized computing environments to the storage system 306 andusers of the storage system 306. Such virtualized computing environmentsmay be embodied, for example, as a virtual machine or other virtualizedcomputer hardware platforms, virtual storage devices, virtualizedcomputer network resources, and so on. Examples of such virtualizedenvironments can include virtual machines that are created to emulate anactual computer, virtualized desktop environments that separate alogical desktop from a physical machine, virtualized file systems thatallow uniform access to different types of concrete file systems, andmany others.

Although the example depicted in FIG. 3A illustrates the storage system306 being coupled for data communications with the cloud servicesprovider 302, in other embodiments the storage system 306 may be part ofa hybrid cloud deployment in which private cloud elements (e.g., privatecloud services, on-premises infrastructure, and so on) and public cloudelements (e.g., public cloud services, infrastructure, and so on thatmay be provided by one or more cloud services providers) are combined toform a single solution, with orchestration among the various platforms.Such a hybrid cloud deployment may leverage hybrid cloud managementsoftware such as, for example, Azure™ Arc from Microsoft™, thatcentralize the management of the hybrid cloud deployment to anyinfrastructure and enable the deployment of services anywhere. In suchan example, the hybrid cloud management software may be configured tocreate, update, and delete resources (both physical and virtual) thatform the hybrid cloud deployment, to allocate compute and storage tospecific workloads, to monitor workloads and resources for performance,policy compliance, updates and patches, security status, or to perform avariety of other tasks.

Readers will appreciate that by pairing the storage systems describedherein with one or more cloud services providers, various offerings maybe enabled. For example, disaster recovery as a service (‘DRaaS’) may beprovided where cloud resources are utilized to protect applications anddata from disruption caused by disaster, including in embodiments wherethe storage systems may serve as the primary data store. In suchembodiments, a total system backup may be taken that allows for businesscontinuity in the event of system failure. In such embodiments, clouddata backup techniques (by themselves or as part of a larger DRaaSsolution) may also be integrated into an overall solution that includesthe storage systems and cloud services providers described herein.

The storage systems described herein, as well as the cloud servicesproviders, may be utilized to provide a wide array of security features.For example, the storage systems may encrypt data at rest (and data maybe sent to and from the storage systems encrypted) and may make use ofKey Management-as-a-Service (‘KMaaS’) to manage encryption keys, keysfor locking and unlocking storage devices, and so on. Likewise, clouddata security gateways or similar mechanisms may be utilized to ensurethat data stored within the storage systems does not improperly end upbeing stored in the cloud as part of a cloud data backup operation.Furthermore, microsegmentation or identity-based-segmentation may beutilized in a data center that includes the storage systems or withinthe cloud services provider, to create secure zones in data centers andcloud deployments that enables the isolation of workloads from oneanother.

For further explanation, FIG. 3B sets forth a diagram of a storagesystem 306 in accordance with some embodiments of the presentdisclosure. Although depicted in less detail, the storage system 306depicted in FIG. 3B may be similar to the storage systems describedabove with reference to FIGS. 1A-1D and FIGS. 2A-2G as the storagesystem may include many of the components described above.

The storage system 306 depicted in FIG. 3B may include a vast amount ofstorage resources 308, which may be embodied in many forms. For example,the storage resources 308 can include nano-RAM or another form ofnonvolatile random access memory that utilizes carbon nanotubesdeposited on a substrate, 3D crosspoint non-volatile memory, flashmemory including single-level cell (‘SLC’) NAND flash, multi-level cell(‘MLC’) NAND flash, triple-level cell (‘TLC’) NAND flash, quad-levelcell (‘QLC’) NAND flash, or others. Likewise, the storage resources 308may include non-volatile magnetoresistive random-access memory (‘MRAM’),including spin transfer torque (‘STT’) MRAM. The example storageresources 308 may alternatively include non-volatile phase-change memory(‘PCM’), quantum memory that allows for the storage and retrieval ofphotonic quantum information, resistive random-access memory (‘ReRAM’),storage class memory (‘SCM’), or other form of storage resources,including any combination of resources described herein. Readers willappreciate that other forms of computer memories and storage devices maybe utilized by the storage systems described above, including DRAM,SRAM, EEPROM, universal memory, and many others. The storage resources308 depicted in FIG. 3A may be embodied in a variety of form factors,including but not limited to, dual in-line memory modules (‘DIMMs’),non-volatile dual in-line memory modules (‘NVDIMMs’), M.2, U.2, andothers.

The storage resources 308 depicted in FIG. 3B may include various formsof SCM. SCM may effectively treat fast, non-volatile memory (e.g., NANDflash) as an extension of DRAM such that an entire dataset may betreated as an in-memory dataset that resides entirely in DRAM. SCM mayinclude non-volatile media such as, for example, NAND flash. Such NANDflash may be accessed utilizing NVMe that can use the PCIe bus as itstransport, providing for relatively low access latencies compared toolder protocols. In fact, the network protocols used for SSDs inall-flash arrays can include NVMe using Ethernet (ROCE, NVME TCP), FibreChannel (NVMe FC), InfiniBand (iWARP), and others that make it possibleto treat fast, non-volatile memory as an extension of DRAM. In view ofthe fact that DRAM is often byte-addressable and fast, non-volatilememory such as NAND flash is block-addressable, a controllersoftware/hardware stack may be needed to convert the block data to thebytes that are stored in the media. Examples of media and software thatmay be used as SCM can include, for example, 3D XPoint, Intel MemoryDrive Technology, Samsung's Z-SSD, and others.

The storage resources 308 depicted in FIG. 3B may also include racetrackmemory (also referred to as domain-wall memory). Such racetrack memorymay be embodied as a form of non-volatile, solid-state memory thatrelies on the intrinsic strength and orientation of the magnetic fieldcreated by an electron as it spins in addition to its electronic charge,in solid-state devices. Through the use of spin-coherent electriccurrent to move magnetic domains along a nanoscopic permalloy wire, thedomains may pass by magnetic read/write heads positioned near the wireas current is passed through the wire, which alter the domains to recordpatterns of bits. In order to create a racetrack memory device, manysuch wires and read/write elements may be packaged together.

The example storage system 306 depicted in FIG. 3B may implement avariety of storage architectures. For example, storage systems inaccordance with some embodiments of the present disclosure may utilizeblock storage where data is stored in blocks, and each block essentiallyacts as an individual hard drive. Storage systems in accordance withsome embodiments of the present disclosure may utilize object storage,where data is managed as objects. Each object may include the dataitself, a variable amount of metadata, and a globally unique identifier,where object storage can be implemented at multiple levels (e.g., devicelevel, system level, interface level). Storage systems in accordancewith some embodiments of the present disclosure utilize file storage inwhich data is stored in a hierarchical structure. Such data may be savedin files and folders, and presented to both the system storing it andthe system retrieving it in the same format.

The example storage system 306 depicted in FIG. 3B may be embodied as astorage system in which additional storage resources can be addedthrough the use of a scale-up model, additional storage resources can beadded through the use of a scale-out model, or through some combinationthereof. In a scale-up model, additional storage may be added by addingadditional storage devices. In a scale-out model, however, additionalstorage nodes may be added to a cluster of storage nodes, where suchstorage nodes can include additional processing resources, additionalnetworking resources, and so on.

The example storage system 306 depicted in FIG. 3B may leverage thestorage resources described above in a variety of different ways. Forexample, some portion of the storage resources may be utilized to serveas a write cache, storage resources within the storage system may beutilized as a read cache, or tiering may be achieved within the storagesystems by placing data within the storage system in accordance with oneor more tiering policies.

The storage system 306 depicted in FIG. 3B also includes communicationsresources 310 that may be useful in facilitating data communicationsbetween components within the storage system 306, as well as datacommunications between the storage system 306 and computing devices thatare outside of the storage system 306, including embodiments where thoseresources are separated by a relatively vast expanse. The communicationsresources 310 may be configured to utilize a variety of differentprotocols and data communication fabrics to facilitate datacommunications between components within the storage systems as well ascomputing devices that are outside of the storage system. For example,the communications resources 310 can include fibre channel (‘FC’)technologies such as FC fabrics and FC protocols that can transport SCSIcommands over FC network, FC over ethernet (‘FCoE’) technologies throughwhich FC frames are encapsulated and transmitted over Ethernet networks,InfiniBand (‘IB’) technologies in which a switched fabric topology isutilized to facilitate transmissions between channel adapters, NVMExpress (‘NVMe’) technologies and NVMe over fabrics (‘NVMeoF’)technologies through which non-volatile storage media attached via a PCIexpress (‘PCIe’) bus may be accessed, and others. In fact, the storagesystems described above may, directly or indirectly, make use ofneutrino communication technologies and devices through whichinformation (including binary information) is transmitted using a beamof neutrinos.

The communications resources 310 can also include mechanisms foraccessing storage resources 308 within the storage system 306 utilizingserial attached SCSI (‘SAS’), serial ATA (‘SATA’) bus interfaces forconnecting storage resources 308 within the storage system 306 to hostbus adapters within the storage system 306, internet small computersystems interface (‘iSCSI’) technologies to provide block-level accessto storage resources 308 within the storage system 306, and othercommunications resources that that may be useful in facilitating datacommunications between components within the storage system 306, as wellas data communications between the storage system 306 and computingdevices that are outside of the storage system 306.

The storage system 306 depicted in FIG. 3B also includes processingresources 312 that may be useful in useful in executing computer programinstructions and performing other computational tasks within the storagesystem 306. The processing resources 312 may include one or more ASICsthat are customized for some particular purpose as well as one or moreCPUs. The processing resources 312 may also include one or more DSPs,one or more FPGAs, one or more systems on a chip (‘SoCs’), or other formof processing resources 312. The storage system 306 may utilize thestorage resources 312 to perform a variety of tasks including, but notlimited to, supporting the execution of software resources 314 that willbe described in greater detail below.

The storage system 306 depicted in FIG. 3B also includes softwareresources 314 that, when executed by processing resources 312 within thestorage system 306, may perform a vast array of tasks. The softwareresources 314 may include, for example, one or more modules of computerprogram instructions that when executed by processing resources 312within the storage system 306 are useful in carrying out various dataprotection techniques. Such data protection techniques may be carriedout, for example, by system software executing on computer hardwarewithin the storage system, by a cloud services provider, or in otherways. Such data protection techniques can include data archiving, databackup, data replication, data snapshotting, data and database cloning,and other data protection techniques.

The software resources 314 may also include software that is useful inimplementing software-defined storage (‘SDS’). In such an example, thesoftware resources 314 may include one or more modules of computerprogram instructions that, when executed, are useful in policy-basedprovisioning and management of data storage that is independent of theunderlying hardware. Such software resources 314 may be useful inimplementing storage virtualization to separate the storage hardwarefrom the software that manages the storage hardware.

The software resources 314 may also include software that is useful infacilitating and optimizing I/O operations that are directed to thestorage system 306. For example, the software resources 314 may includesoftware modules that perform various data reduction techniques such as,for example, data compression, data deduplication, and others. Thesoftware resources 314 may include software modules that intelligentlygroup together I/O operations to facilitate better usage of theunderlying storage resource 308, software modules that perform datamigration operations to migrate from within a storage system, as well assoftware modules that perform other functions. Such software resources314 may be embodied as one or more software containers or in many otherways.

For further explanation, FIG. 3C sets forth an example of a cloud-basedstorage system 318 in accordance with some embodiments of the presentdisclosure. In the example depicted in FIG. 3C, the cloud-based storagesystem 318 is created entirely in a cloud computing environment 316 suchas, for example, Amazon Web Services (‘AWS’)™, Microsoft Azure™, GoogleCloud Platform™, IBM Cloud™, Oracle Cloud™, and others. The cloud-basedstorage system 318 may be used to provide services similar to theservices that may be provided by the storage systems described above.

The cloud-based storage system 318 depicted in FIG. 3C includes twocloud computing instances 320, 322 that each are used to support theexecution of a storage controller application 324, 326. The cloudcomputing instances 320, 322 may be embodied, for example, as instancesof cloud computing resources (e.g., virtual machines) that may beprovided by the cloud computing environment 316 to support the executionof software applications such as the storage controller application 324,326. For example, each of the cloud computing instances 320, 322 mayexecute on an Azure VM, where each Azure VM may include high speedtemporary storage that may be leveraged as a cache (e.g., as a readcache). In one embodiment, the cloud computing instances 320, 322 may beembodied as Amazon Elastic Compute Cloud (‘EC2’) instances. In such anexample, an Amazon Machine Image (‘AMI’) that includes the storagecontroller application 324, 326 may be booted to create and configure avirtual machine that may execute the storage controller application 324,326.

In the example method depicted in FIG. 3C, the storage controllerapplication 324, 326 may be embodied as a module of computer programinstructions that, when executed, carries out various storage tasks. Forexample, the storage controller application 324, 326 may be embodied asa module of computer program instructions that, when executed, carriesout the same tasks as the controllers 110A, 110B in FIG. 1A describedabove such as writing data to the cloud-based storage system 318,erasing data from the cloud-based storage system 318, retrieving datafrom the cloud-based storage system 318, monitoring and reporting ofdisk utilization and performance, performing redundancy operations, suchas RAID or RAID-like data redundancy operations, compressing data,encrypting data, deduplicating data, and so forth. Readers willappreciate that because there are two cloud computing instances 320, 322that each include the storage controller application 324, 326, in someembodiments one cloud computing instance 320 may operate as the primarycontroller as described above while the other cloud computing instance322 may operate as the secondary controller as described above. Readerswill appreciate that the storage controller application 324, 326depicted in FIG. 3C may include identical source code that is executedwithin different cloud computing instances 320, 322 such as distinct EC2instances.

Readers will appreciate that other embodiments that do not include aprimary and secondary controller are within the scope of the presentdisclosure. For example, each cloud computing instance 320, 322 mayoperate as a primary controller for some portion of the address spacesupported by the cloud-based storage system 318, each cloud computinginstance 320, 322 may operate as a primary controller where theservicing of I/O operations directed to the cloud-based storage system318 are divided in some other way, and so on. In fact, in otherembodiments where costs savings may be prioritized over performancedemands, only a single cloud computing instance may exist that containsthe storage controller application.

The cloud-based storage system 318 depicted in FIG. 3C includes cloudcomputing instances 340 a, 340 b, 340 n with local storage 330, 334,338. The cloud computing instances 340 a, 340 b, 340 n may be embodied,for example, as instances of cloud computing resources that may beprovided by the cloud computing environment 316 to support the executionof software applications. The cloud computing instances 340 a, 340 b,340 n of FIG. 3C may differ from the cloud computing instances 320, 322described above as the cloud computing instances 340 a, 340 b, 340 n ofFIG. 3C have local storage 330, 334, 338 resources whereas the cloudcomputing instances 320, 322 that support the execution of the storagecontroller application 324, 326 need not have local storage resources.The cloud computing instances 340 a, 340 b, 340 n with local storage330, 334, 338 may be embodied, for example, as EC2 M5 instances thatinclude one or more SSDs, as EC2 R5 instances that include one or moreSSDs, as EC2 I3 instances that include one or more SSDs, and so on. Insome embodiments, the local storage 330, 334, 338 must be embodied assolid-state storage (e.g., SSDs) rather than storage that makes use ofhard disk drives.

In the example depicted in FIG. 3C, each of the cloud computinginstances 340 a, 340 b, 340 n with local storage 330, 334, 338 caninclude a software daemon 328, 332, 336 that, when executed by a cloudcomputing instance 340 a, 340 b, 340 n can present itself to the storagecontroller applications 324, 326 as if the cloud computing instance 340a, 340 b, 340 n were a physical storage device (e.g., one or more SSDs).In such an example, the software daemon 328, 332, 336 may includecomputer program instructions similar to those that would normally becontained on a storage device such that the storage controllerapplications 324, 326 can send and receive the same commands that astorage controller would send to storage devices. In such a way, thestorage controller applications 324, 326 may include code that isidentical to (or substantially identical to) the code that would beexecuted by the controllers in the storage systems described above. Inthese and similar embodiments, communications between the storagecontroller applications 324, 326 and the cloud computing instances 340a, 340 b, 340 n with local storage 330, 334, 338 may utilize iSCSI, NVMeover TCP, messaging, a custom protocol, or in some other mechanism.

In the example depicted in FIG. 3C, each of the cloud computinginstances 340 a, 340 b, 340 n with local storage 330, 334, 338 may alsobe coupled to block storage 342, 344, 346 that is offered by the cloudcomputing environment 316 such as, for example, as Amazon Elastic BlockStore (‘EBS’) volumes. In such an example, the block storage 342, 344,346 that is offered by the cloud computing environment 316 may beutilized in a manner that is similar to how the NVRAM devices describedabove are utilized, as the software daemon 328, 332, 336 (or some othermodule) that is executing within a particular cloud comping instance 340a, 340 b, 340 n may, upon receiving a request to write data, initiate awrite of the data to its attached EBS volume as well as a write of thedata to its local storage 330, 334, 338 resources. In some alternativeembodiments, data may only be written to the local storage 330, 334, 338resources within a particular cloud comping instance 340 a, 340 b, 340n. In an alternative embodiment, rather than using the block storage342, 344, 346 that is offered by the cloud computing environment 316 asNVRAM, actual RAM on each of the cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338 may be used as NVRAM, therebydecreasing network utilization costs that would be associated with usingan EBS volume as the NVRAM. In yet another embodiment, high performanceblock storage resources such as one or more Azure Ultra Disks may beutilized as the NVRAM.

The storage controller applications 324, 326 may be used to performvarious tasks such as deduplicating the data contained in the request,compressing the data contained in the request, determining where to thewrite the data contained in the request, and so on, before ultimatelysending a request to write a deduplicated, encrypted, or otherwisepossibly updated version of the data to one or more of the cloudcomputing instances 340 a, 340 b, 340 n with local storage 330, 334,338. Either cloud computing instance 320, 322, in some embodiments, mayreceive a request to read data from the cloud-based storage system 318and may ultimately send a request to read data to one or more of thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338.

When a request to write data is received by a particular cloud computinginstance 340 a, 340 b, 340 n with local storage 330, 334, 338, thesoftware daemon 328, 332, 336 may be configured to not only write thedata to its own local storage 330, 334, 338 resources and anyappropriate block storage 342, 344, 346 resources, but the softwaredaemon 328, 332, 336 may also be configured to write the data tocloud-based object storage 348 that is attached to the particular cloudcomputing instance 340 a, 340 b, 340 n. The cloud-based object storage348 that is attached to the particular cloud computing instance 340 a,340 b, 340 n may be embodied, for example, as Amazon Simple StorageService (‘S3’). In other embodiments, the cloud computing instances 320,322 that each include the storage controller application 324, 326 mayinitiate the storage of the data in the local storage 330, 334, 338 ofthe cloud computing instances 340 a, 340 b, 340 n and the cloud-basedobject storage 348. In other embodiments, rather than using both thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338 (also referred to herein as ‘virtual drives’) and thecloud-based object storage 348 to store data, a persistent storage layermay be implemented in other ways. For example, one or more Azure Ultradisks may be used to persistently store data (e.g., after the data hasbeen written to the NVRAM layer).

While the local storage 330, 334, 338 resources and the block storage342, 344, 346 resources that are utilized by the cloud computinginstances 340 a, 340 b, 340 n may support block-level access, thecloud-based object storage 348 that is attached to the particular cloudcomputing instance 340 a, 340 b, 340 n supports only object-basedaccess. The software daemon 328, 332, 336 may therefore be configured totake blocks of data, package those blocks into objects, and write theobjects to the cloud-based object storage 348 that is attached to theparticular cloud computing instance 340 a, 340 b, 340 n.

Consider an example in which data is written to the local storage 330,334, 338 resources and the block storage 342, 344, 346 resources thatare utilized by the cloud computing instances 340 a, 340 b, 340 n in 1MB blocks. In such an example, assume that a user of the cloud-basedstorage system 318 issues a request to write data that, after beingcompressed and deduplicated by the storage controller application 324,326 results in the need to write 5 MB of data. In such an example,writing the data to the local storage 330, 334, 338 resources and theblock storage 342, 344, 346 resources that are utilized by the cloudcomputing instances 340 a, 340 b, 340 n is relatively straightforward as5 blocks that are 1 MB in size are written to the local storage 330,334, 338 resources and the block storage 342, 344, 346 resources thatare utilized by the cloud computing instances 340 a, 340 b, 340 n. Insuch an example, the software daemon 328, 332, 336 may also beconfigured to create five objects containing distinct 1 MB chunks of thedata. As such, in some embodiments, each object that is written to thecloud-based object storage 348 may be identical (or nearly identical) insize. Readers will appreciate that in such an example, metadata that isassociated with the data itself may be included in each object (e.g.,the first 1 MB of the object is data and the remaining portion ismetadata associated with the data). Readers will appreciate that thecloud-based object storage 348 may be incorporated into the cloud-basedstorage system 318 to increase the durability of the cloud-based storagesystem 318.

In some embodiments, all data that is stored by the cloud-based storagesystem 318 may be stored in both: 1) the cloud-based object storage 348,and 2) at least one of the local storage 330, 334, 338 resources orblock storage 342, 344, 346 resources that are utilized by the cloudcomputing instances 340 a, 340 b, 340 n. In such embodiments, the localstorage 330, 334, 338 resources and block storage 342, 344, 346resources that are utilized by the cloud computing instances 340 a, 340b, 340 n may effectively operate as cache that generally includes alldata that is also stored in S3, such that all reads of data may beserviced by the cloud computing instances 340 a, 340 b, 340 n withoutrequiring the cloud computing instances 340 a, 340 b, 340 n to accessthe cloud-based object storage 348. Readers will appreciate that inother embodiments, however, all data that is stored by the cloud-basedstorage system 318 may be stored in the cloud-based object storage 348,but less than all data that is stored by the cloud-based storage system318 may be stored in at least one of the local storage 330, 334, 338resources or block storage 342, 344, 346 resources that are utilized bythe cloud computing instances 340 a, 340 b, 340 n. In such an example,various policies may be utilized to determine which subset of the datathat is stored by the cloud-based storage system 318 should reside inboth: 1) the cloud-based object storage 348, and 2) at least one of thelocal storage 330, 334, 338 resources or block storage 342, 344, 346resources that are utilized by the cloud computing instances 340 a, 340b, 340 n.

One or more modules of computer program instructions that are executingwithin the cloud-based storage system 318 (e.g., a monitoring modulethat is executing on its own EC2 instance) may be designed to handle thefailure of one or more of the cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338. In such an example, themonitoring module may handle the failure of one or more of the cloudcomputing instances 340 a, 340 b, 340 n with local storage 330, 334, 338by creating one or more new cloud computing instances with localstorage, retrieving data that was stored on the failed cloud computinginstances 340 a, 340 b, 340 n from the cloud-based object storage 348,and storing the data retrieved from the cloud-based object storage 348in local storage on the newly created cloud computing instances. Readerswill appreciate that many variants of this process may be implemented.

Readers will appreciate that various performance aspects of thecloud-based storage system 318 may be monitored (e.g., by a monitoringmodule that is executing in an EC2 instance) such that the cloud-basedstorage system 318 can be scaled-up or scaled-out as needed. Forexample, if the cloud computing instances 320, 322 that are used tosupport the execution of a storage controller application 324, 326 areundersized and not sufficiently servicing the I/O requests that areissued by users of the cloud-based storage system 318, a monitoringmodule may create a new, more powerful cloud computing instance (e.g., acloud computing instance of a type that includes more processing power,more memory, etc. . . . ) that includes the storage controllerapplication such that the new, more powerful cloud computing instancecan begin operating as the primary controller. Likewise, if themonitoring module determines that the cloud computing instances 320, 322that are used to support the execution of a storage controllerapplication 324, 326 are oversized and that cost savings could be gainedby switching to a smaller, less powerful cloud computing instance, themonitoring module may create a new, less powerful (and less expensive)cloud computing instance that includes the storage controllerapplication such that the new, less powerful cloud computing instancecan begin operating as the primary controller.

The storage systems described above may carry out intelligent databackup techniques through which data stored in the storage system may becopied and stored in a distinct location to avoid data loss in the eventof equipment failure or some other form of catastrophe. For example, thestorage systems described above may be configured to examine each backupto avoid restoring the storage system to an undesirable state. Consideran example in which malware infects the storage system. In such anexample, the storage system may include software resources 314 that canscan each backup to identify backups that were captured before themalware infected the storage system and those backups that were capturedafter the malware infected the storage system. In such an example, thestorage system may restore itself from a backup that does not includethe malware—or at least not restore the portions of a backup thatcontained the malware. In such an example, the storage system mayinclude software resources 314 that can scan each backup to identify thepresences of malware (or a virus, or some other undesirable), forexample, by identifying write operations that were serviced by thestorage system and originated from a network subnet that is suspected tohave delivered the malware, by identifying write operations that wereserviced by the storage system and originated from a user that issuspected to have delivered the malware, by identifying write operationsthat were serviced by the storage system and examining the content ofthe write operation against fingerprints of the malware, and in manyother ways.

Readers will further appreciate that the backups (often in the form ofone or more snapshots) may also be utilized to perform rapid recovery ofthe storage system. Consider an example in which the storage system isinfected with ransomware that locks users out of the storage system. Insuch an example, software resources 314 within the storage system may beconfigured to detect the presence of ransomware and may be furtherconfigured to restore the storage system to a point-in-time, using theretained backups, prior to the point-in-time at which the ransomwareinfected the storage system. In such an example, the presence ofransomware may be explicitly detected through the use of software toolsutilized by the system, through the use of a key (e.g., a USB drive)that is inserted into the storage system, or in a similar way. Likewise,the presence of ransomware may be inferred in response to systemactivity meeting a predetermined fingerprint such as, for example, noreads or writes coming into the system for a predetermined period oftime.

Readers will appreciate that the various components described above maybe grouped into one or more optimized computing packages as convergedinfrastructures. Such converged infrastructures may include pools ofcomputers, storage and networking resources that can be shared bymultiple applications and managed in a collective manner usingpolicy-driven processes. Such converged infrastructures may beimplemented with a converged infrastructure reference architecture, withstandalone appliances, with a software driven hyper-converged approach(e.g., hyper-converged infrastructures), or in other ways.

Readers will appreciate that the storage systems described in thisdisclosure may be useful for supporting various types of softwareapplications. In fact, the storage systems may be ‘application aware’ inthe sense that the storage systems may obtain, maintain, or otherwisehave access to information describing connected applications (e.g.,applications that utilize the storage systems) to optimize the operationof the storage system based on intelligence about the applications andtheir utilization patterns. For example, the storage system may optimizedata layouts, optimize caching behaviors, optimize ‘QoS’ levels, orperform some other optimization that is designed to improve the storageperformance that is experienced by the application.

As an example of one type of application that may be supported by thestorage systems describe herein, the storage system 306 may be useful insupporting artificial intelligence (‘AI’) applications, databaseapplications, XOps projects (e.g., DevOps projects, DataOps projects,MLOps projects, ModelOps projects, PlatformOps projects), electronicdesign automation tools, event-driven software applications, highperformance computing applications, simulation applications, high-speeddata capture and analysis applications, machine learning applications,media production applications, media serving applications, picturearchiving and communication systems (‘PACS’) applications, softwaredevelopment applications, virtual reality applications, augmentedreality applications, and many other types of applications by providingstorage resources to such applications.

In view of the fact that the storage systems include compute resources,storage resources, and a wide variety of other resources, the storagesystems may be well suited to support applications that are resourceintensive such as, for example, AI applications. AI applications may bedeployed in a variety of fields, including: predictive maintenance inmanufacturing and related fields, healthcare applications such aspatient data & risk analytics, retail and marketing deployments (e.g.,search advertising, social media advertising), supply chains solutions,fintech solutions such as business analytics & reporting tools,operational deployments such as real-time analytics tools, applicationperformance management tools, IT infrastructure management tools, andmany others.

Such AI applications may enable devices to perceive their environmentand take actions that maximize their chance of success at some goal.Examples of such AI applications can include IBM Watson™, MicrosoftOxford™, Google DeepMind™, Baidu Minwa™, and others.

The storage systems described above may also be well suited to supportother types of applications that are resource intensive such as, forexample, machine learning applications. Machine learning applicationsmay perform various types of data analysis to automate analytical modelbuilding. Using algorithms that iteratively learn from data, machinelearning applications can enable computers to learn without beingexplicitly programmed. One particular area of machine learning isreferred to as reinforcement learning, which involves taking suitableactions to maximize reward in a particular situation.

In addition to the resources already described, the storage systemsdescribed above may also include graphics processing units (‘GPUs’),occasionally referred to as visual processing unit (‘VPUs’). Such GPUsmay be embodied as specialized electronic circuits that rapidlymanipulate and alter memory to accelerate the creation of images in aframe buffer intended for output to a display device. Such GPUs may beincluded within any of the computing devices that are part of thestorage systems described above, including as one of many individuallyscalable components of a storage system, where other examples ofindividually scalable components of such storage system can includestorage components, memory components, compute components (e.g., CPUs,FPGAs, ASICs), networking components, software components, and others.In addition to GPUs, the storage systems described above may alsoinclude neural network processors (‘NNPs’) for use in various aspects ofneural network processing. Such NNPs may be used in place of (or inaddition to) GPUs and may also be independently scalable.

As described above, the storage systems described herein may beconfigured to support artificial intelligence applications, machinelearning applications, big data analytics applications, and many othertypes of applications. The rapid growth in these sort of applications isbeing driven by three technologies: deep learning (DL), GPU processors,and Big Data. Deep learning is a computing model that makes use ofmassively parallel neural networks inspired by the human brain. Insteadof experts handcrafting software, a deep learning model writes its ownsoftware by learning from lots of examples. Such GPUs may includethousands of cores that are well-suited to run algorithms that looselyrepresent the parallel nature of the human brain.

Advances in deep neural networks, including the development ofmulti-layer neural networks, have ignited a new wave of algorithms andtools for data scientists to tap into their data with artificialintelligence (AI). With improved algorithms, larger data sets, andvarious frameworks (including open-source software libraries for machinelearning across a range of tasks), data scientists are tackling new usecases like autonomous driving vehicles, natural language processing andunderstanding, computer vision, machine reasoning, strong AI, and manyothers. Applications of such techniques may include: machine andvehicular object detection, identification and avoidance; visualrecognition, classification and tagging; algorithmic financial tradingstrategy performance management; simultaneous localization and mapping;predictive maintenance of high-value machinery; prevention against cybersecurity threats, expertise automation; image recognition andclassification; question answering; robotics; text analytics(extraction, classification) and text generation and translation; andmany others. Applications of AI techniques has materialized in a widearray of products include, for example, Amazon Echo's speech recognitiontechnology that allows users to talk to their machines, GoogleTranslate™ which allows for machine-based language translation,Spotify's Discover Weekly that provides recommendations on new songs andartists that a user may like based on the user's usage and trafficanalysis, Quill's text generation offering that takes structured dataand turns it into narrative stories, Chatbots that provide real-time,contextually specific answers to questions in a dialog format, and manyothers.

Data is the heart of modern AI and deep learning algorithms. Beforetraining can begin, one problem that must be addressed revolves aroundcollecting the labeled data that is crucial for training an accurate AImodel. A full scale AI deployment may be required to continuouslycollect, clean, transform, label, and store large amounts of data.Adding additional high quality data points directly translates to moreaccurate models and better insights. Data samples may undergo a seriesof processing steps including, but not limited to: 1) ingesting the datafrom an external source into the training system and storing the data inraw form, 2) cleaning and transforming the data in a format convenientfor training, including linking data samples to the appropriate label,3) exploring parameters and models, quickly testing with a smallerdataset, and iterating to converge on the most promising models to pushinto the production cluster, 4) executing training phases to selectrandom batches of input data, including both new and older samples, andfeeding those into production GPU servers for computation to updatemodel parameters, and 5) evaluating including using a holdback portionof the data not used in training in order to evaluate model accuracy onthe holdout data. This lifecycle may apply for any type of parallelizedmachine learning, not just neural networks or deep learning. Forexample, standard machine learning frameworks may rely on CPUs insteadof GPUs but the data ingest and training workflows may be the same.Readers will appreciate that a single shared storage data hub creates acoordination point throughout the lifecycle without the need for extradata copies among the ingest, preprocessing, and training stages. Rarelyis the ingested data used for only one purpose, and shared storage givesthe flexibility to train multiple different models or apply traditionalanalytics to the data.

Readers will appreciate that each stage in the AI data pipeline may havevarying requirements from the data hub (e.g., the storage system orcollection of storage systems). Scale-out storage systems must deliveruncompromising performance for all manner of access types andpatterns—from small, metadata-heavy to large files, from random tosequential access patterns, and from low to high concurrency. Thestorage systems described above may serve as an ideal AI data hub as thesystems may service unstructured workloads. In the first stage, data isideally ingested and stored on to the same data hub that followingstages will use, in order to avoid excess data copying. The next twosteps can be done on a standard compute server that optionally includesa GPU, and then in the fourth and last stage, full training productionjobs are run on powerful GPU-accelerated servers. Often, there is aproduction pipeline alongside an experimental pipeline operating on thesame dataset. Further, the GPU-accelerated servers can be usedindependently for different models or joined together to train on onelarger model, even spanning multiple systems for distributed training.If the shared storage tier is slow, then data must be copied to localstorage for each phase, resulting in wasted time staging data ontodifferent servers. The ideal data hub for the AI training pipelinedelivers performance similar to data stored locally on the server nodewhile also having the simplicity and performance to enable all pipelinestages to operate concurrently.

In order for the storage systems described above to serve as a data hubor as part of an AI deployment, in some embodiments the storage systemsmay be configured to provide DMA between storage devices that areincluded in the storage systems and one or more GPUs that are used in anAI or big data analytics pipeline. The one or more GPUs may be coupledto the storage system, for example, via NVMe-over-Fabrics (‘NVMe-oF’)such that bottlenecks such as the host CPU can be bypassed and thestorage system (or one of the components contained therein) can directlyaccess GPU memory. In such an example, the storage systems may leverageAPI hooks to the GPUs to transfer data directly to the GPUs. Forexample, the GPUs may be embodied as Nvidia™ GPUs and the storagesystems may support GPUDirect Storage (‘GDS’) software, or have similarproprietary software, that enables the storage system to transfer datato the GPUs via RDMA or similar mechanism.

Although the preceding paragraphs discuss deep learning applications,readers will appreciate that the storage systems described herein mayalso be part of a distributed deep learning (‘DDL’) platform to supportthe execution of DDL algorithms. The storage systems described above mayalso be paired with other technologies such as TensorFlow, anopen-source software library for dataflow programming across a range oftasks that may be used for machine learning applications such as neuralnetworks, to facilitate the development of such machine learning models,applications, and so on.

The storage systems described above may also be used in a neuromorphiccomputing environment. Neuromorphic computing is a form of computingthat mimics brain cells. To support neuromorphic computing, anarchitecture of interconnected “neurons” replace traditional computingmodels with low-powered signals that go directly between neurons formore efficient computation. Neuromorphic computing may make use ofvery-large-scale integration (VLSI) systems containing electronic analogcircuits to mimic neuro-biological architectures present in the nervoussystem, as well as analog, digital, mixed-mode analog/digital VLSI, andsoftware systems that implement models of neural systems for perception,motor control, or multisensory integration.

Readers will appreciate that the storage systems described above may beconfigured to support the storage or use of (among other types of data)blockchains and derivative items such as, for example, open sourceblockchains and related tools that are part of the IBM™ Hyperledgerproject, permissioned blockchains in which a certain number of trustedparties are allowed to access the block chain, blockchain products thatenable developers to build their own distributed ledger projects, andothers. Blockchains and the storage systems described herein may beleveraged to support on-chain storage of data as well as off-chainstorage of data.

Off-chain storage of data can be implemented in a variety of ways andcan occur when the data itself is not stored within the blockchain. Forexample, in one embodiment, a hash function may be utilized and the dataitself may be fed into the hash function to generate a hash value. Insuch an example, the hashes of large pieces of data may be embeddedwithin transactions, instead of the data itself. Readers will appreciatethat, in other embodiments, alternatives to blockchains may be used tofacilitate the decentralized storage of information. For example, onealternative to a blockchain that may be used is a blockweave. Whileconventional blockchains store every transaction to achieve validation,a blockweave permits secure decentralization without the usage of theentire chain, thereby enabling low cost on-chain storage of data. Suchblockweaves may utilize a consensus mechanism that is based on proof ofaccess (PoA) and proof of work (PoW).

The storage systems described above may, either alone or in combinationwith other computing devices, be used to support in-memory computingapplications. In-memory computing involves the storage of information inRAM that is distributed across a cluster of computers. Readers willappreciate that the storage systems described above, especially thosethat are configurable with customizable amounts of processing resources,storage resources, and memory resources (e.g., those systems in whichblades that contain configurable amounts of each type of resource), maybe configured in a way so as to provide an infrastructure that cansupport in-memory computing. Likewise, the storage systems describedabove may include component parts (e.g., NVDIMMs, 3D crosspoint storagethat provide fast random access memory that is persistent) that canactually provide for an improved in-memory computing environment ascompared to in-memory computing environments that rely on RAMdistributed across dedicated servers.

In some embodiments, the storage systems described above may beconfigured to operate as a hybrid in-memory computing environment thatincludes a universal interface to all storage media (e.g., RAM, flashstorage, 3D crosspoint storage). In such embodiments, users may have noknowledge regarding the details of where their data is stored but theycan still use the same full, unified API to address data. In suchembodiments, the storage system may (in the background) move data to thefastest layer available—including intelligently placing the data independence upon various characteristics of the data or in dependenceupon some other heuristic. In such an example, the storage systems mayeven make use of existing products such as Apache Ignite and GridGain tomove data between the various storage layers, or the storage systems maymake use of custom software to move data between the various storagelayers. The storage systems described herein may implement variousoptimizations to improve the performance of in-memory computing such as,for example, having computations occur as close to the data as possible.

Readers will further appreciate that in some embodiments, the storagesystems described above may be paired with other resources to supportthe applications described above. For example, one infrastructure couldinclude primary compute in the form of servers and workstations whichspecialize in using General-purpose computing on graphics processingunits (‘GPGPU’) to accelerate deep learning applications that areinterconnected into a computation engine to train parameters for deepneural networks. Each system may have Ethernet external connectivity,InfiniBand external connectivity, some other form of externalconnectivity, or some combination thereof. In such an example, the GPUscan be grouped for a single large training or used independently totrain multiple models. The infrastructure could also include a storagesystem such as those described above to provide, for example, ascale-out all-flash file or object store through which data can beaccessed via high-performance protocols such as NFS, S3, and so on. Theinfrastructure can also include, for example, redundant top-of-rackEthernet switches connected to storage and compute via ports in MLAGport channels for redundancy. The infrastructure could also includeadditional compute in the form of whitebox servers, optionally withGPUs, for data ingestion, pre-processing, and model debugging. Readerswill appreciate that additional infrastructures are also be possible.

Readers will appreciate that the storage systems described above, eitheralone or in coordination with other computing machinery may beconfigured to support other AI related tools. For example, the storagesystems may make use of tools like ONXX or other open neural networkexchange formats that make it easier to transfer models written indifferent AI frameworks. Likewise, the storage systems may be configuredto support tools like Amazon's Gluon that allow developers to prototype,build, and train deep learning models. In fact, the storage systemsdescribed above may be part of a larger platform, such as IBM™ CloudPrivate for Data, that includes integrated data science, dataengineering and application building services.

Readers will further appreciate that the storage systems described abovemay also be deployed as an edge solution. Such an edge solution may bein place to optimize cloud computing systems by performing dataprocessing at the edge of the network, near the source of the data. Edgecomputing can push applications, data and computing power (i.e.,services) away from centralized points to the logical extremes of anetwork. Through the use of edge solutions such as the storage systemsdescribed above, computational tasks may be performed using the computeresources provided by such storage systems, data may be storage usingthe storage resources of the storage system, and cloud-based servicesmay be accessed through the use of various resources of the storagesystem (including networking resources). By performing computationaltasks on the edge solution, storing data on the edge solution, andgenerally making use of the edge solution, the consumption of expensivecloud-based resources may be avoided and, in fact, performanceimprovements may be experienced relative to a heavier reliance oncloud-based resources.

While many tasks may benefit from the utilization of an edge solution,some particular uses may be especially suited for deployment in such anenvironment. For example, devices like drones, autonomous cars, robots,and others may require extremely rapid processing—so fast, in fact, thatsending data up to a cloud environment and back to receive dataprocessing support may simply be too slow. As an additional example,some IoT devices such as connected video cameras may not be well-suitedfor the utilization of cloud-based resources as it may be impractical(not only from a privacy perspective, security perspective, or afinancial perspective) to send the data to the cloud simply because ofthe pure volume of data that is involved. As such, many tasks thatreally on data processing, storage, or communications may be bettersuited by platforms that include edge solutions such as the storagesystems described above.

The storage systems described above may alone, or in combination withother computing resources, serves as a network edge platform thatcombines compute resources, storage resources, networking resources,cloud technologies and network virtualization technologies, and so on.As part of the network, the edge may take on characteristics similar toother network facilities, from the customer premise and backhaulaggregation facilities to Points of Presence (PoPs) and regional datacenters. Readers will appreciate that network workloads, such as VirtualNetwork Functions (VNFs) and others, will reside on the network edgeplatform. Enabled by a combination of containers and virtual machines,the network edge platform may rely on controllers and schedulers thatare no longer geographically co-located with the data processingresources. The functions, as microservices, may split into controlplanes, user and data planes, or even state machines, allowing forindependent optimization and scaling techniques to be applied. Such userand data planes may be enabled through increased accelerators, boththose residing in server platforms, such as FPGAs and Smart NICs, andthrough SDN-enabled merchant silicon and programmable ASICs.

The storage systems described above may also be optimized for use in bigdata analytics, including being leveraged as part of a composable dataanalytics pipeline where containerized analytics architectures, forexample, make analytics capabilities more composable. Big data analyticsmay be generally described as the process of examining large and varieddata sets to uncover hidden patterns, unknown correlations, markettrends, customer preferences and other useful information that can helporganizations make more-informed business decisions. As part of thatprocess, semi-structured and unstructured data such as, for example,internet clickstream data, web server logs, social media content, textfrom customer emails and survey responses, mobile-phone call-detailrecords, IoT sensor data, and other data may be converted to astructured form.

The storage systems described above may also support (includingimplementing as a system interface) applications that perform tasks inresponse to human speech. For example, the storage systems may supportthe execution intelligent personal assistant applications such as, forexample, Amazon's Alexa™, Apple Siri™, Google Voice™, Samsung Bixby™,Microsoft Cortana™, and others. While the examples described in theprevious sentence make use of voice as input, the storage systemsdescribed above may also support chatbots, talkbots, chatterbots, orartificial conversational entities or other applications that areconfigured to conduct a conversation via auditory or textual methods.Likewise, the storage system may actually execute such an application toenable a user such as a system administrator to interact with thestorage system via speech. Such applications are generally capable ofvoice interaction, music playback, making to-do lists, setting alarms,streaming podcasts, playing audiobooks, and providing weather, traffic,and other real time information, such as news, although in embodimentsin accordance with the present disclosure, such applications may beutilized as interfaces to various system management operations.

The storage systems described above may also implement AI platforms fordelivering on the vision of self-driving storage. Such AI platforms maybe configured to deliver global predictive intelligence by collectingand analyzing large amounts of storage system telemetry data points toenable effortless management, analytics and support. In fact, suchstorage systems may be capable of predicting both capacity andperformance, as well as generating intelligent advice on workloaddeployment, interaction and optimization. Such AI platforms may beconfigured to scan all incoming storage system telemetry data against alibrary of issue fingerprints to predict and resolve incidents inreal-time, before they impact customer environments, and captureshundreds of variables related to performance that are used to forecastperformance load.

The storage systems described above may support the serialized orsimultaneous execution of artificial intelligence applications, machinelearning applications, data analytics applications, datatransformations, and other tasks that collectively may form an AIladder. Such an AI ladder may effectively be formed by combining suchelements to form a complete data science pipeline, where existdependencies between elements of the AI ladder. For example, AI mayrequire that some form of machine learning has taken place, machinelearning may require that some form of analytics has taken place,analytics may require that some form of data and informationarchitecting has taken place, and so on. As such, each element may beviewed as a rung in an AI ladder that collectively can form a completeand sophisticated AI solution.

The storage systems described above may also, either alone or incombination with other computing environments, be used to deliver an AIeverywhere experience where AI permeates wide and expansive aspects ofbusiness and life. For example, AI may play an important role in thedelivery of deep learning solutions, deep reinforcement learningsolutions, artificial general intelligence solutions, autonomousvehicles, cognitive computing solutions, commercial UAVs or drones,conversational user interfaces, enterprise taxonomies, ontologymanagement solutions, machine learning solutions, smart dust, smartrobots, smart workplaces, and many others.

The storage systems described above may also, either alone or incombination with other computing environments, be used to deliver a widerange of transparently immersive experiences (including those that usedigital twins of various “things” such as people, places, processes,systems, and so on) where technology can introduce transparency betweenpeople, businesses, and things. Such transparently immersive experiencesmay be delivered as augmented reality technologies, connected homes,virtual reality technologies, brain-computer interfaces, humanaugmentation technologies, nanotube electronics, volumetric displays, 4Dprinting technologies, or others.

The storage systems described above may also, either alone or incombination with other computing environments, be used to support a widevariety of digital platforms. Such digital platforms can include, forexample, 5G wireless systems and platforms, digital twin platforms, edgecomputing platforms, IoT platforms, quantum computing platforms,serverless PaaS, software-defined security, neuromorphic computingplatforms, and so on.

The storage systems described above may also be part of a multi-cloudenvironment in which multiple cloud computing and storage services aredeployed in a single heterogeneous architecture. In order to facilitatethe operation of such a multi-cloud environment, DevOps tools may bedeployed to enable orchestration across clouds. Likewise, continuousdevelopment and continuous integration tools may be deployed tostandardize processes around continuous integration and delivery, newfeature rollout and provisioning cloud workloads. By standardizing theseprocesses, a multi-cloud strategy may be implemented that enables theutilization of the best provider for each workload.

The storage systems described above may be used as a part of a platformto enable the use of crypto-anchors that may be used to authenticate aproduct's origins and contents to ensure that it matches a blockchainrecord associated with the product. Similarly, as part of a suite oftools to secure data stored on the storage system, the storage systemsdescribed above may implement various encryption technologies andschemes, including lattice cryptography. Lattice cryptography caninvolve constructions of cryptographic primitives that involve lattices,either in the construction itself or in the security proof. Unlikepublic-key schemes such as the RSA, Diffie-Hellman or Elliptic-Curvecryptosystems, which are easily attacked by a quantum computer, somelattice-based constructions appear to be resistant to attack by bothclassical and quantum computers.

A quantum computer is a device that performs quantum computing. Quantumcomputing is computing using quantum-mechanical phenomena, such assuperposition and entanglement. Quantum computers differ fromtraditional computers that are based on transistors, as such traditionalcomputers require that data be encoded into binary digits (bits), eachof which is always in one of two definite states (0 or 1). In contrastto traditional computers, quantum computers use quantum bits, which canbe in superpositions of states. A quantum computer maintains a sequenceof qubits, where a single qubit can represent a one, a zero, or anyquantum superposition of those two qubit states. A pair of qubits can bein any quantum superposition of 4 states, and three qubits in anysuperposition of 8 states. A quantum computer with n qubits cangenerally be in an arbitrary superposition of up to 2{circumflex over( )}n different states simultaneously, whereas a traditional computercan only be in one of these states at any one time. A quantum Turingmachine is a theoretical model of such a computer.

The storage systems described above may also be paired withFPGA-accelerated servers as part of a larger AI or ML infrastructure.Such FPGA-accelerated servers may reside near (e.g., in the same datacenter) the storage systems described above or even incorporated into anappliance that includes one or more storage systems, one or moreFPGA-accelerated servers, networking infrastructure that supportscommunications between the one or more storage systems and the one ormore FPGA-accelerated servers, as well as other hardware and softwarecomponents. Alternatively, FPGA-accelerated servers may reside within acloud computing environment that may be used to perform compute-relatedtasks for AI and ML jobs. Any of the embodiments described above may beused to collectively serve as a FPGA-based AI or ML platform. Readerswill appreciate that, in some embodiments of the FPGA-based AI or MLplatform, the FPGAs that are contained within the FPGA-acceleratedservers may be reconfigured for different types of ML models (e.g.,LSTMs, CNNs, GRUs). The ability to reconfigure the FPGAs that arecontained within the FPGA-accelerated servers may enable theacceleration of a ML or AI application based on the most optimalnumerical precision and memory model being used. Readers will appreciatethat by treating the collection of FPGA-accelerated servers as a pool ofFPGAs, any CPU in the data center may utilize the pool of FPGAs as ashared hardware microservice, rather than limiting a server to dedicatedaccelerators plugged into it.

The FPGA-accelerated servers and the GPU-accelerated servers describedabove may implement a model of computing where, rather than keeping asmall amount of data in a CPU and running a long stream of instructionsover it as occurred in more traditional computing models, the machinelearning model and parameters are pinned into the high-bandwidth on-chipmemory with lots of data streaming though the high-bandwidth on-chipmemory. FPGAs may even be more efficient than GPUs for this computingmodel, as the FPGAs can be programmed with only the instructions neededto run this kind of computing model.

The storage systems described above may be configured to provideparallel storage, for example, through the use of a parallel file systemsuch as BeeGFS. Such parallel files systems may include a distributedmetadata architecture. For example, the parallel file system may includea plurality of metadata servers across which metadata is distributed, aswell as components that include services for clients and storageservers.

The systems described above can support the execution of a wide array ofsoftware applications. Such software applications can be deployed in avariety of ways, including container-based deployment models.Containerized applications may be managed using a variety of tools. Forexample, containerized applications may be managed using Docker Swarm,Kubernetes, and others. Containerized applications may be used tofacilitate a serverless, cloud native computing deployment andmanagement model for software applications. In support of a serverless,cloud native computing deployment and management model for softwareapplications, containers may be used as part of an event handlingmechanisms (e.g., AWS Lambdas) such that various events cause acontainerized application to be spun up to operate as an event handler.

The systems described above may be deployed in a variety of ways,including being deployed in ways that support fifth generation (‘5G’)networks. 5G networks may support substantially faster datacommunications than previous generations of mobile communicationsnetworks and, as a consequence may lead to the disaggregation of dataand computing resources as modern massive data centers may become lessprominent and may be replaced, for example, by more-local, micro datacenters that are close to the mobile-network towers. The systemsdescribed above may be included in such local, micro data centers andmay be part of or paired to multi-access edge computing (‘MEC’) systems.Such MEC systems may enable cloud computing capabilities and an ITservice environment at the edge of the cellular network. By runningapplications and performing related processing tasks closer to thecellular customer, network congestion may be reduced and applicationsmay perform better.

The storage systems described above may also be configured to implementNVMe Zoned Namespaces. Through the use of NVMe Zoned Namespaces, thelogical address space of a namespace is divided into zones. Each zoneprovides a logical block address range that must be written sequentiallyand explicitly reset before rewriting, thereby enabling the creation ofnamespaces that expose the natural boundaries of the device and offloadmanagement of internal mapping tables to the host. In order to implementNVMe Zoned Name Spaces (‘ZNS’), ZNS SSDs or some other form of zonedblock devices may be utilized that expose a namespace logical addressspace using zones. With the zones aligned to the internal physicalproperties of the device, several inefficiencies in the placement ofdata can be eliminated. In such embodiments, each zone may be mapped,for example, to a separate application such that functions like wearlevelling and garbage collection could be performed on a per-zone orper-application basis rather than across the entire device. In order tosupport ZNS, the storage controllers described herein may be configuredwith to interact with zoned block devices through the usage of, forexample, the Linux™ kernel zoned block device interface or other tools.

The storage systems described above may also be configured to implementzoned storage in other ways such as, for example, through the usage ofshingled magnetic recording (SMR) storage devices. In examples wherezoned storage is used, device-managed embodiments may be deployed wherethe storage devices hide this complexity by managing it in the firmware,presenting an interface like any other storage device. Alternatively,zoned storage may be implemented via a host-managed embodiment thatdepends on the operating system to know how to handle the drive, andonly write sequentially to certain regions of the drive. Zoned storagemay similarly be implemented using a host-aware embodiment in which acombination of a drive managed and host managed implementation isdeployed.

The storage systems described herein may be used to form a data lake. Adata lake may operate as the first place that an organization's dataflows to, where such data may be in a raw format. Metadata tagging maybe implemented to facilitate searches of data elements in the data lake,especially in embodiments where the data lake contains multiple storesof data, in formats not easily accessible or readable (e.g.,unstructured data, semi-structured data, structured data). From the datalake, data may go downstream to a data warehouse where data may bestored in a more processed, packaged, and consumable format. The storagesystems described above may also be used to implement such a datawarehouse. In addition, a data mart or data hub may allow for data thatis even more easily consumed, where the storage systems described abovemay also be used to provide the underlying storage resources necessaryfor a data mart or data hub. In embodiments, queries the data lake mayrequire a schema-on-read approach, where data is applied to a plan orschema as it is pulled out of a stored location, rather than as it goesinto the stored location.

The storage systems described herein may also be configured implement arecovery point objective (‘RPO’), which may be establish by a user,established by an administrator, established as a system default,established as part of a storage class or service that the storagesystem is participating in the delivery of, or in some other way. A“recovery point objective” is a goal for the maximum time differencebetween the last update to a source dataset and the last recoverablereplicated dataset update that would be correctly recoverable, given areason to do so, from a continuously or frequently updated copy of thesource dataset. An update is correctly recoverable if it properly takesinto account all updates that were processed on the source dataset priorto the last recoverable replicated dataset update.

In synchronous replication, the RPO would be zero, meaning that undernormal operation, all completed updates on the source dataset should bepresent and correctly recoverable on the copy dataset. In best effortnearly synchronous replication, the RPO can be as low as a few seconds.In snapshot-based replication, the RPO can be roughly calculated as theinterval between snapshots plus the time to transfer the modificationsbetween a previous already transferred snapshot and the most recentto-be-replicated snapshot.

If updates accumulate faster than they are replicated, then an RPO canbe missed. If more data to be replicated accumulates between twosnapshots, for snapshot-based replication, than can be replicatedbetween taking the snapshot and replicating that snapshot's cumulativeupdates to the copy, then the RPO can be missed. If, again insnapshot-based replication, data to be replicated accumulates at afaster rate than could be transferred in the time between subsequentsnapshots, then replication can start to fall further behind which canextend the miss between the expected recovery point objective and theactual recovery point that is represented by the last correctlyreplicated update.

The storage systems described above may also be part of a shared nothingstorage cluster. In a shared nothing storage cluster, each node of thecluster has local storage and communicates with other nodes in thecluster through networks, where the storage used by the cluster is (ingeneral) provided only by the storage connected to each individual node.A collection of nodes that are synchronously replicating a dataset maybe one example of a shared nothing storage cluster, as each storagesystem has local storage and communicates to other storage systemsthrough a network, where those storage systems do not (in general) usestorage from somewhere else that they share access to through some kindof interconnect. In contrast, some of the storage systems describedabove are themselves built as a shared-storage cluster, since there aredrive shelves that are shared by the paired controllers. Other storagesystems described above, however, are built as a shared nothing storagecluster, as all storage is local to a particular node (e.g., a blade)and all communication is through networks that link the compute nodestogether.

In other embodiments, other forms of a shared nothing storage clustercan include embodiments where any node in the cluster has a local copyof all storage they need, and where data is mirrored through asynchronous style of replication to other nodes in the cluster either toensure that the data isn't lost or because other nodes are also usingthat storage. In such an embodiment, if a new cluster node needs somedata, that data can be copied to the new node from other nodes that havecopies of the data.

In some embodiments, mirror-copy-based shared storage clusters may storemultiple copies of all the cluster's stored data, with each subset ofdata replicated to a particular set of nodes, and different subsets ofdata replicated to different sets of nodes. In some variations,embodiments may store all of the cluster's stored data in all nodes,whereas in other variations nodes may be divided up such that a firstset of nodes will all store the same set of data and a second, differentset of nodes will all store a different set of data.

Readers will appreciate that RAFT-based databases (e.g., etcd) mayoperate like shared-nothing storage clusters where all RAFT nodes storeall data. The amount of data stored in a RAFT cluster, however, may belimited so that extra copies don't consume too much storage. A containerserver cluster might also be able to replicate all data to all clusternodes, presuming the containers don't tend to be too large and theirbulk data (the data manipulated by the applications that run in thecontainers) is stored elsewhere such as in an S3 cluster or an externalfile server. In such an example, the container storage may be providedby the cluster directly through its shared-nothing storage model, withthose containers providing the images that form the executionenvironment for parts of an application or service.

For further explanation, FIG. 3D illustrates an exemplary computingdevice 350 that may be specifically configured to perform one or more ofthe processes described herein. As shown in FIG. 3D, computing device350 may include a communication interface 352, a processor 354, astorage device 356, and an input/output (“I/O”) module 358communicatively connected one to another via a communicationinfrastructure 360. While an exemplary computing device 350 is shown inFIG. 3D, the components illustrated in FIG. 3D are not intended to belimiting. Additional or alternative components may be used in otherembodiments. Components of computing device 350 shown in FIG. 3D willnow be described in additional detail.

Communication interface 352 may be configured to communicate with one ormore computing devices. Examples of communication interface 352 include,without limitation, a wired network interface (such as a networkinterface card), a wireless network interface (such as a wirelessnetwork interface card), a modem, an audio/video connection, and anyother suitable interface.

Processor 354 generally represents any type or form of processing unitcapable of processing data and/or interpreting, executing, and/ordirecting execution of one or more of the instructions, processes,and/or operations described herein. Processor 354 may perform operationsby executing computer-executable instructions 362 (e.g., an application,software, code, and/or other executable data instance) stored in storagedevice 356.

Storage device 356 may include one or more data storage media, devices,or configurations and may employ any type, form, and combination of datastorage media and/or device. For example, storage device 356 mayinclude, but is not limited to, any combination of the non-volatilemedia and/or volatile media described herein. Electronic data, includingdata described herein, may be temporarily and/or permanently stored instorage device 356. For example, data representative ofcomputer-executable instructions 362 configured to direct processor 354to perform any of the operations described herein may be stored withinstorage device 356. In some examples, data may be arranged in one ormore databases residing within storage device 356.

I/O module 358 may include one or more I/O modules configured to receiveuser input and provide user output. I/O module 358 may include anyhardware, firmware, software, or combination thereof supportive of inputand output capabilities. For example, I/O module 358 may includehardware and/or software for capturing user input, including, but notlimited to, a keyboard or keypad, a touchscreen component (e.g.,touchscreen display), a receiver (e.g., an RF or infrared receiver),motion sensors, and/or one or more input buttons.

I/O module 358 may include one or more devices for presenting output toa user, including, but not limited to, a graphics engine, a display(e.g., a display screen), one or more output drivers (e.g., displaydrivers), one or more audio speakers, and one or more audio drivers. Incertain embodiments, I/O module 358 is configured to provide graphicaldata to a display for presentation to a user. The graphical data may berepresentative of one or more graphical user interfaces and/or any othergraphical content as may serve a particular implementation. In someexamples, any of the systems, computing devices, and/or other componentsdescribed herein may be implemented by computing device 350.

For further explanation, FIG. 3E illustrates an example of a fleet ofstorage systems 376 for providing storage services (also referred toherein as ‘data services’). The fleet of storage systems 376 depicted inFIG. 3 includes a plurality of storage systems 374 a, 374 b, 374 c, 374d, 374 n that may each be similar to the storage systems describedherein. The storage systems 374 a, 374 b, 374 c, 374 d, 374 n in thefleet of storage systems 376 may be embodied as identical storagesystems or as different types of storage systems. For example, two ofthe storage systems 374 a, 374 n depicted in FIG. 3E are depicted asbeing cloud-based storage systems, as the resources that collectivelyform each of the storage systems 374 a, 374 n are provided by distinctcloud services providers 370, 372. For example, the first cloud servicesprovider 370 may be Amazon AWS whereas the second cloud servicesprovider 372 is Microsoft Azure™, although in other embodiments one ormore public clouds, private clouds, or combinations thereof may be usedto provide the underlying resources that are used to form a particularstorage system in the fleet of storage systems 376.

The example depicted in FIG. 3E includes an edge management service 382for delivering storage services in accordance with some embodiments ofthe present disclosure. The storage services (also referred to herein as‘data services’) that are delivered may include, for example, servicesto provide a certain amount of storage to a consumer, services toprovide storage to a consumer in accordance with a predetermined servicelevel agreement, services to provide storage to a consumer in accordancewith predetermined regulatory requirements, and many others.

The edge management service 382 depicted in FIG. 3E may be embodied, forexample, as one or more modules of computer program instructionsexecuting on computer hardware such as one or more computer processors.Alternatively, the edge management service 382 may be embodied as one ormore modules of computer program instructions executing on a virtualizedexecution environment such as one or more virtual machines, in one ormore containers, or in some other way. In other embodiments, the edgemanagement service 382 may be embodied as a combination of theembodiments described above, including embodiments where the one or moremodules of computer program instructions that are included in the edgemanagement service 382 are distributed across multiple physical orvirtual execution environments.

The edge management service 382 may operate as a gateway for providingstorage services to storage consumers, where the storage servicesleverage storage offered by one or more storage systems 374 a, 374 b,374 c, 374 d, 374 n. For example, the edge management service 382 may beconfigured to provide storage services to host devices 378 a, 378 b, 378c, 378 d, 378 n that are executing one or more applications that consumethe storage services. In such an example, the edge management service382 may operate as a gateway between the host devices 378 a, 378 b, 378c, 378 d, 378 n and the storage systems 374 a, 374 b, 374 c, 374 d, 374n, rather than requiring that the host devices 378 a, 378 b, 378 c, 378d, 378 n directly access the storage systems 374 a, 374 b, 374 c, 374 d,374 n.

The edge management service 382 of FIG. 3E exposes a storage servicesmodule 380 to the host devices 378 a, 378 b, 378 c, 378 d, 378 n of FIG.3E, although in other embodiments the edge management service 382 mayexpose the storage services module 380 to other consumers of the variousstorage services. The various storage services may be presented toconsumers via one or more user interfaces, via one or more APIs, orthrough some other mechanism provided by the storage services module380. As such, the storage services module 380 depicted in FIG. 3E may beembodied as one or more modules of computer program instructionsexecuting on physical hardware, on a virtualized execution environment,or combinations thereof, where executing such modules causes enables aconsumer of storage services to be offered, select, and access thevarious storage services.

The edge management service 382 of FIG. 3E also includes a systemmanagement services module 384. The system management services module384 of FIG. 3E includes one or more modules of computer programinstructions that, when executed, perform various operations incoordination with the storage systems 374 a, 374 b, 374 c, 374 d, 374 nto provide storage services to the host devices 378 a, 378 b, 378 c, 378d, 378 n. The system management services module 384 may be configured,for example, to perform tasks such as provisioning storage resourcesfrom the storage systems 374 a, 374 b, 374 c, 374 d, 374 n via one ormore APIs exposed by the storage systems 374 a, 374 b, 374 c, 374 d, 374n, migrating datasets or workloads amongst the storage systems 374 a,374 b, 374 c, 374 d, 374 n via one or more APIs exposed by the storagesystems 374 a, 374 b, 374 c, 374 d, 374 n, setting one or more tunableparameters (i.e., one or more configurable settings) on the storagesystems 374 a, 374 b, 374 c, 374 d, 374 n via one or more APIs exposedby the storage systems 374 a, 374 b, 374 c, 374 d, 374 n, and so on. Forexample, many of the services described below relate to embodimentswhere the storage systems 374 a, 374 b, 374 c, 374 d, 374 n areconfigured to operate in some way. In such examples, the systemmanagement services module 384 may be responsible for using APIs (orsome other mechanism) provided by the storage systems 374 a, 374 b, 374c, 374 d, 374 n to configure the storage systems 374 a, 374 b, 374 c,374 d, 374 n to operate in the ways described below.

In addition to configuring the storage systems 374 a, 374 b, 374 c, 374d, 374 n, the edge management service 382 itself may be configured toperform various tasks required to provide the various storage services.Consider an example in which the storage service includes a servicethat, when selected and applied, causes personally identifiableinformation (‘PII’) contained in a dataset to be obfuscated when thedataset is accessed. In such an example, the storage systems 374 a, 374b, 374 c, 374 d, 374 n may be configured to obfuscate PII when servicingread requests directed to the dataset. Alternatively, the storagesystems 374 a, 374 b, 374 c, 374 d, 374 n may service reads by returningdata that includes the PII, but the edge management service 382 itselfmay obfuscate the PII as the data is passed through the edge managementservice 382 on its way from the storage systems 374 a, 374 b, 374 c, 374d, 374 n to the host devices 378 a, 378 b, 378 c, 378 d, 378 n.

The storage systems 374 a, 374 b, 374 c, 374 d, 374 n depicted in FIG.3E may be embodied as one or more of the storage systems described abovewith reference to FIGS. 1A-3D, including variations thereof. In fact,the storage systems 374 a, 374 b, 374 c, 374 d, 374 n may serve as apool of storage resources where the individual components in that poolhave different performance characteristics, different storagecharacteristics, and so on. For example, one of the storage systems 374a may be a cloud-based storage system, another storage system 374 b maybe a storage system that provides block storage, another storage system374 c may be a storage system that provides file storage, anotherstorage system 374 d may be a relatively high-performance storage systemwhile another storage system 374 n may be a relatively low-performancestorage system, and so on. In alternative embodiments, only a singlestorage system may be present.

The storage systems 374 a, 374 b, 374 c, 374 d, 374 n depicted in FIG.3E may also be organized into different failure domains so that thefailure of one storage system 374 a should be totally unrelated to thefailure of another storage system 374 b. For example, each of thestorage systems may receive power from independent power systems, eachof the storage systems may be coupled for data communications overindependent data communications networks, and so on. Furthermore, thestorage systems in a first failure domain may be accessed via a firstgateway whereas storage systems in a second failure domain may beaccessed via a second gateway. For example, the first gateway may be afirst instance of the edge management service 382 and the second gatewaymay be a second instance of the edge management service 382, includingembodiments where each instance is distinct, or each instance is part ofa distributed edge management service 382.

As an illustrative example of available storage services, storageservices may be presented to a user that are associated with differentlevels of data protection. For example, storage services may bepresented to the user that, when selected and enforced, guarantee theuser that data associated with that user will be protected such thatvarious recovery point objectives (‘RPO’) can be guaranteed. A firstavailable storage service may ensure, for example, that some datasetassociated with the user will be protected such that any data that ismore than 5 seconds old can be recovered in the event of a failure ofthe primary data store whereas a second available storage service mayensure that the dataset that is associated with the user will beprotected such that any data that is more than 5 minutes old can berecovered in the event of a failure of the primary data store.

An additional example of storage services that may be presented to auser, selected by a user, and ultimately applied to a dataset associatedwith the user can include one or more data compliance services. Suchdata compliance services may be embodied, for example, as services thatmay be provided to consumers (i.e., a user) the data compliance servicesto ensure that the user's datasets are managed in a way to adhere tovarious regulatory requirements. For example, one or more datacompliance services may be offered to a user to ensure that the user'sdatasets are managed in a way so as to adhere to the General DataProtection Regulation (‘GDPR’), one or data compliance services may beoffered to a user to ensure that the user's datasets are managed in away so as to adhere to the Sarbanes-Oxley Act of 2002 (‘SOX’), or one ormore data compliance services may be offered to a user to ensure thatthe user's datasets are managed in a way so as to adhere to some otherregulatory act. In addition, the one or more data compliance servicesmay be offered to a user to ensure that the user's datasets are managedin a way so as to adhere to some non-governmental guidance (e.g., toadhere to best practices for auditing purposes), the one or more datacompliance services may be offered to a user to ensure that the user'sdatasets are managed in a way so as to adhere to a particular clients ororganizations requirements, and so on.

Consider an example in which a particular data compliance service isdesigned to ensure that a user's datasets are managed in a way so as toadhere to the requirements set forth in the GDPR. While a listing of allrequirements of the GDPR can be found in the regulation itself, for thepurposes of illustration, an example requirement set forth in the GDPRrequires that pseudonymization processes must be applied to stored datain order to transform personal data in such a way that the resultingdata cannot be attributed to a specific data subject without the use ofadditional information. For example, data encryption techniques can beapplied to render the original data unintelligible, and such dataencryption techniques cannot be reversed without access to the correctdecryption key. As such, the GDPR may require that the decryption key bekept separately from the pseudonymized data. One particular datacompliance service may be offered to ensure adherence to therequirements set forth in this paragraph.

In order to provide this particular data compliance service, the datacompliance service may be presented to a user (e.g., via a GUI) andselected by the user. In response to receiving the selection of theparticular data compliance service, one or more storage servicespolicies may be applied to a dataset associated with the user to carryout the particular data compliance service. For example, a storageservices policy may be applied requiring that the dataset be encryptedprior to be stored in a storage system, prior to being stored in a cloudenvironment, or prior to being stored elsewhere. In order to enforcethis policy, a requirement may be enforced not only requiring that thedataset be encrypted when stored, but a requirement may be put in placerequiring that the dataset be encrypted prior to transmitting thedataset (e.g., sending the dataset to another party). In such anexample, a storage services policy may also be put in place requiringthat any encryption keys used to encrypt the dataset are not stored onthe same system that stores the dataset itself. Readers will appreciatethat many other forms of data compliance services may be offered andimplemented in accordance with embodiments of the present disclosure.

The storage systems 374 a, 374 b, 374 c, 374 d, 374 n in the fleet ofstorage systems 376 may be managed collectively, for example, by one ormore fleet management modules. The fleet management modules may be partof or separate from the system management services module 384 depictedin FIG. 3E. The fleet management modules may perform tasks such asmonitoring the health of each storage system in the fleet, initiatingupdates or upgrades on one or more storage systems in the fleet,migrating workloads for loading balancing or other performance purposes,and many other tasks. As such, and for many other reasons, the storagesystems 374 a, 374 b, 374 c, 374 d, 374 n may be coupled to each othervia one or more data communications links in order to exchange databetween the storage systems 374 a, 374 b, 374 c, 374 d, 374 n.

The storage systems described herein may support various forms of datareplication. For example, two or more of the storage systems maysynchronously replicate a dataset between each other. In synchronousreplication, distinct copies of a particular dataset may be maintainedby multiple storage systems, but all accesses (e.g., a read) of thedataset should yield consistent results regardless of which storagesystem the access was directed to. For example, a read directed to anyof the storage systems that are synchronously replicating the datasetshould return identical results. As such, while updates to the versionof the dataset need not occur at exactly the same time, precautions mustbe taken to ensure consistent accesses to the dataset. For example, ifan update (e.g., a write) that is directed to the dataset is received bya first storage system, the update may only be acknowledged as beingcompleted if all storage systems that are synchronously replicating thedataset have applied the update to their copies of the dataset. In suchan example, synchronous replication may be carried out through the useof I/O forwarding (e.g., a write received at a first storage system isforwarded to a second storage system), communications between thestorage systems (e.g., each storage system indicating that it hascompleted the update), or in other ways.

In other embodiments, a dataset may be replicated through the use ofcheckpoints. In checkpoint-based replication (also referred to as‘nearly synchronous replication’), a set of updates to a dataset (e.g.,one or more write operations directed to the dataset) may occur betweendifferent checkpoints, such that a dataset has been updated to aspecific checkpoint only if all updates to the dataset prior to thespecific checkpoint have been completed. Consider an example in which afirst storage system stores a live copy of a dataset that is beingaccessed by users of the dataset. In this example, assume that thedataset is being replicated from the first storage system to a secondstorage system using checkpoint-based replication. For example, thefirst storage system may send a first checkpoint (at time t=0) to thesecond storage system, followed by a first set of updates to thedataset, followed by a second checkpoint (at time t=1), followed by asecond set of updates to the dataset, followed by a third checkpoint (attime t=2). In such an example, if the second storage system hasperformed all updates in the first set of updates but has not yetperformed all updates in the second set of updates, the copy of thedataset that is stored on the second storage system may be up-to-dateuntil the second checkpoint. Alternatively, if the second storage systemhas performed all updates in both the first set of updates and thesecond set of updates, the copy of the dataset that is stored on thesecond storage system may be up-to-date until the third checkpoint.Readers will appreciate that various types of checkpoints may be used(e.g., metadata only checkpoints), checkpoints may be spread out basedon a variety of factors (e.g., time, number of operations, an RPOsetting), and so on.

In other embodiments, a dataset may be replicated through snapshot-basedreplication (also referred to as ‘asynchronous replication’). Insnapshot-based replication, snapshots of a dataset may be sent from areplication source such as a first storage system to a replicationtarget such as a second storage system. In such an embodiment, eachsnapshot may include the entire dataset or a subset of the dataset suchas, for example, only the portions of the dataset that have changedsince the last snapshot was sent from the replication source to thereplication target. Readers will appreciate that snapshots may be senton-demand, based on a policy that takes a variety of factors intoconsideration (e.g., time, number of operations, an RPO setting), or insome other way.

The storage systems described above may, either alone or in combination,by configured to serve as a continuous data protection store. Acontinuous data protection store is a feature of a storage system thatrecords updates to a dataset in such a way that consistent images ofprior contents of the dataset can be accessed with a low timegranularity (often on the order of seconds, or even less), andstretching back for a reasonable period of time (often hours or days).These allow access to very recent consistent points in time for thedataset, and also allow access to access to points in time for a datasetthat might have just preceded some event that, for example, caused partsof the dataset to be corrupted or otherwise lost, while retaining closeto the maximum number of updates that preceded that event. Conceptually,they are like a sequence of snapshots of a dataset taken very frequentlyand kept for a long period of time, though continuous data protectionstores are often implemented quite differently from snapshots. A storagesystem implementing a data continuous data protection store may furtherprovide a means of accessing these points in time, accessing one or moreof these points in time as snapshots or as cloned copies, or revertingthe dataset back to one of those recorded points in time.

Over time, to reduce overhead, some points in the time held in acontinuous data protection store can be merged with other nearby pointsin time, essentially deleting some of these points in time from thestore. This can reduce the capacity needed to store updates. It may alsobe possible to convert a limited number of these points in time intolonger duration snapshots. For example, such a store might keep a lowgranularity sequence of points in time stretching back a few hours fromthe present, with some points in time merged or deleted to reduceoverhead for up to an additional day. Stretching back in the pastfurther than that, some of these points in time could be converted tosnapshots representing consistent point-in-time images from only everyfew hours.

Although some embodiments are described largely in the context of astorage system, readers of skill in the art will recognize thatembodiments of the present disclosure may also take the form of acomputer program product disposed upon computer readable storage mediafor use with any suitable processing system. Such computer readablestorage media may be any storage medium for machine-readableinformation, including magnetic media, optical media, solid-state media,or other suitable media. Examples of such media include magnetic disksin hard drives or diskettes, compact disks for optical drives, magnetictape, and others as will occur to those of skill in the art. Personsskilled in the art will immediately recognize that any computer systemhaving suitable programming means will be capable of executing the stepsdescribed herein as embodied in a computer program product. Personsskilled in the art will recognize also that, although some of theembodiments described in this specification are oriented to softwareinstalled and executing on computer hardware, nevertheless, alternativeembodiments implemented as firmware or as hardware are well within thescope of the present disclosure.

In some examples, a non-transitory computer-readable medium storingcomputer-readable instructions may be provided in accordance with theprinciples described herein. The instructions, when executed by aprocessor of a computing device, may direct the processor and/orcomputing device to perform one or more operations, including one ormore of the operations described herein. Such instructions may be storedand/or transmitted using any of a variety of known computer-readablemedia.

A non-transitory computer-readable medium as referred to herein mayinclude any non-transitory storage medium that participates in providingdata (e.g., instructions) that may be read and/or executed by acomputing device (e.g., by a processor of a computing device). Forexample, a non-transitory computer-readable medium may include, but isnot limited to, any combination of non-volatile storage media and/orvolatile storage media. Exemplary non-volatile storage media include,but are not limited to, read-only memory, flash memory, a solid-statedrive, a magnetic storage device (e.g., a hard disk, a floppy disk,magnetic tape, etc.), ferroelectric random-access memory (“RAM”), and anoptical disc (e.g., a compact disc, a digital video disc, a Blu-raydisc, etc.). Exemplary volatile storage media include, but are notlimited to, RAM (e.g., dynamic RAM).

For further explanation, FIG. 4 sets forth a flowchart illustrating anexample method of workload planning in a storage system (408) accordingto some embodiments of the present disclosure. The storage systems (402,406, 408) depicted in FIG. 4 may be similar to the storage systemsdescribed in the previous figures, as each storage system (402, 406,408) may include any combination of the components as described withreference to the other figures described herein.

The example method depicted in FIG. 4 includes generating (410), independence upon data (404) collected from a plurality of storage systems(402, 406), a load model (412) that predicts performance load on thestorage system (408) based on characteristics of workloads (420, 422,424) executing on the storage system (408). The data (404) collectedfrom a plurality of storage systems (402, 406) may be embodied, forexample, as telemetry data that is periodically sent from the storagesystems (402, 406) to a centralized management service (notillustrated). Such telemetry data may include information that is usefulfor monitoring the operation of the storage system that sends the dataincluding, for example, information describing various performancecharacteristics of the storage system, information describing variousworkloads that are executing on the storage system, and other types ofinformation. The information describing various performancecharacteristics of the storage system can include, for example, thenumber of IOPS being serviced by the storage system, the utilizationrates of various computing resources (e.g., CPU utilization) within thestorage system, the utilization rates of various networking resources(e.g., network bandwidth utilization) within the storage system, theutilization rates of various storage resources (e.g., NVRAM utilization)within the storage system, and many others. Likewise, the informationdescribing various workloads that are executing on the storage systemcan include, for example, information describing the number of IOPSbeing generated by a particular workload, overwrite rates for I/Ooperations that are being generated by the workload, the amount of readbandwidth that is being consumed by I/O operations generated by theworkload, and many others. As such, an examination of the telemetry datacan reveal characteristics of the workloads (420, 422, 424) executing onthe storage system (408). The characteristics of the workloads (420,422, 424) executing on the storage system (408) can include, forexample, information describing the number of IOPS being generated bythe workload, overwrite rates for I/O operations that are beinggenerated by the workload, the amount of read bandwidth that is beingconsumed by I/O operations generated by the workload, and many others.

In the example method depicted in FIG. 4, a load model (412) thatpredicts performance load on the storage system (408) based oncharacteristics of workloads (420, 422, 424) executing on the storagesystem (408) may be generated (410). The term ‘performance load’ usedherein may refer to a measure of load on a storage system that isgenerated in dependence upon multiple system metrics. For example, theperformance load on the storage system (408) may be generated independence upon the amount of read bandwidth being serviced by thestorage system, the amount of write bandwidth being serviced by thestorage system, the amount of IOPS being serviced by the storage system,the amount of computing load being placed on the storage system, theamount of data transfer load being placed on the storage system, andmany other factors. In such an example, the performance load on thestorage system (408) may be calculated according to some formula thattakes as inputs the weighted or unweighted combination of such factorsdescribed in the preceding sentence. The performance load on the storagesystem (408) can therefore, in some embodiments, represent a singlemeasure of load on a storage system that is generated in dependence uponmultiple system metrics.

In the example method depicted in FIG. 4, generating (410) the loadmodel (412) that predicts performance load on the storage system (408)based on characteristics of workloads (420, 422, 424) executing on thestorage system (408) in dependence upon data (404) collected from aplurality of storage systems (402, 406) may be carried out, for example,through the use of machine learning techniques. In such an example,machine learning algorithms may be fed with information describingvarious performance characteristics of various storage systems (asextracted from the telemetry data) and information describing variousworkloads that are executing on various storage systems (as extractedfrom the telemetry data) to identify correlations between the amount ofperformance load that was placed on a particular storage system giventhe characteristics of the workloads that were executing on theparticular storage system at the same point in time. In such an example,a load model (412) may be created for a variety of different storagesystem configurations. For example, load models may be created forstorage systems that have different hardware configurations, load modelsmay be created for storage systems that have different softwareconfigurations, load models may be created for storage systems that havedifferent configuration settings, or any combination thereof. As such,each particular load model that is generated may be specific to aparticular combination of hardware, software, configuration settings, orother attributes of a particular storage system configuration. In otherembodiments, each particular load model may be to a subset of suchattributes of a particular storage system configuration.

The example method depicted in FIG. 4 also includes generating (414),for one or more of workloads (420, 422, 424), predicted characteristics(416) of the one or more workloads (420, 422, 424). In the examplemethod depicted in FIG. 4, generating (414) predicted characteristics(416) of the one or more workloads (420, 422, 424) can includeperforming a time-series analysis of each workload (420, 422, 424).Readers will appreciate that the telemetry data described herein may notonly be useful for enabling a centralized management service to monitorthe operation of the storage system that sends the data, but suchtelemetry data may also be useful for identifying trends associated withthe workloads themselves. As such, an examination of the telemetry datacan be used to generate trending information for the workloadsincluding, for example, information describing the rate at which thenumber of TOPS being generated by the workload has been changing, therate at which overwrite rates for I/O operations that are beinggenerated by the workload are changing, the rate at which the amount ofread bandwidth that is being consumed by I/O operations generated by theworkload is changing, and many others. In such a way, predictedcharacteristics (416) of the one or more workloads (420, 422, 424) maybe generated (414) by extrapolating identified trends out over a periodof time in the future.

Consider an example in which telemetry data gathered from a plurality ofstorage systems (402, 406) indicates that, on average, the amount of CPUresources required to support a virtual desktop infrastructure workloaddoubles every three years. In such an example, if a particular workload(422) that is executing on the storage system (408) is a virtual desktopinfrastructure workload, generating (414) predicted characteristics(416) of such a workload may be carried out, at least in part, bydetermining the amount of CPU resources currently required to supportthe particular workload (422) and assuming that the amount of CPUresources that will be required to support the particular workload (422)in the future will double every three years. In such a way, the loaddemands created by each workload may be projected to some point in thefuture.

The example method depicted in FIG. 4 also includes predicting (418)performance load on the storage system (408) in dependence upon the loadmodel (412) and the predicted characteristics (416) of the one or moreworkloads (420, 422, 424). Predicting (418) performance load on thestorage system (408) may be carried out, for example, by utilizing thepredicted characteristics (416) of the one or more workloads (420, 422,424) that will be supported by the storage system as inputs to the loadmodel (412) associated with the storage system (408). In such a way, ifthe one or more workloads (420, 422, 424) do change over time aspredicted (thereby resulting in a change to the amount of systemresources that are consumed by the workloads), and the load model (412)can accurately predict how well the storage system (408) could supportthe workloads in their new state, the performance load on the storagesystem (408) can be accurately predicted.

Continuing with the example described above in which the storage system(408) is supporting a virtual desktop infrastructure workload andtelemetry data gathered from a plurality of storage systems (402, 406)indicates that, on average, the amount of CPU resources required tosupport a virtual desktop infrastructure workload doubles every threeyears, assume that the load model (412) for the storage system (408)indicated that doubling the amount of CPU resources on the storagesystem (408) would result in a 20% increase in total performance load onthe storage system (408). In such an example, absent any other changesto the storage system (408) or the workloads (420, 422, 424) supportedby the storage system (408), predicting (418) performance load on thestorage system (408) in dependence upon the load model (412) and thepredicted characteristics (416) of the one or more workloads (420, 422,424) would result in a prediction that the total performance load on thestorage system (408) would increase by 20% in three years. Thisinformation could be used for a variety of reasons, as will be expandedupon below.

Readers will appreciate that predicting (418) performance load on thestorage system (408), a load model (412) may be used that was developedfor storage systems that most closely resemble the storage system (408)whose performance load is being predicted. Consider an example in whichload models are constructed for systems using some combination of threesystem attributes: model number, system software version number, andstorage capacity. In such an example, assume that the table below mapsvarious load models with various system configurations:

TABLE 1 Model Mapping Table Load System Software Storage Model ID ModelNum. Version Capacity 1 1 Any Any 2 1 1 Any 3 1 2 Any 4 1 1 250-499 TB 51 1 500 TB-1.5 PB 6 1 2 250-499 TB 7 1 2 500 TB-1.5 PB 8 2 Any Any 9 2 1Any 10 2 1 Any

In this example, assume that the storage system (408) whose performanceload is being predicted is a storage system with a model number of ‘1’,that is running version ‘2’ of system software, that has a storagecapacity of 1 PB. In such an example, load model ‘5’ is the load modelthat has been developed for storage systems that most closely resemblethe storage system (408) whose performance load is being predictedwould. As such, load model ‘5’ would be utilized when predicting (418)performance load on the storage system (408) in dependence upon the loadmodel (412) and the predicted characteristics (416) of the one or moreworkloads (420, 422, 424). In an example where the storage system (408)whose performance load is being predicted is a storage system with amodel number of ‘1’, that is running version ‘2’ of system software,that has a storage capacity of 400 TB, however, load model ‘4’ may beutilized. Readers will appreciate that in situations in which a fit thatmatches all criterion is not available, catch-all models (e.g., models1, 2, 3, 8, 9, 10) may be utilized.

For further explanation, FIG. 5 sets forth a flowchart illustrating anadditional example method of workload planning in a storage systemaccording to some embodiments of the present disclosure. The examplemethod depicted in FIG. 5 is similar to the example methods describedabove, as the example method depicted in FIG. 5 also includes generating(410) a load model (412), generating (414) predicted characteristics(416) of the one or more workloads (420, 422, 424), and predicting (418)performance load on the storage system (408) in dependence upon the loadmodel (412) and the predicted characteristics (416) of the one or moreworkloads (420, 422, 424).

The example method depicted in FIG. 5 also includes receiving (502)information describing one or more modifications to the storage system(408). The modifications to the storage system (408) may include, forexample, upgrading from one version of system software to anotherversion of system software, adding storage devices, removing storagedevices, replacing existing storage devices in the storage system (408)with improved (e.g., denser, faster) storage devices, modifying theamount of computing resources within the storage system, modifying thetype or amount of networking resources within the storage system (408),or any combination of these or other modifications. The informationdescribing one or more modifications to the storage system (408) may beembodied, for example, as a system inventory list or in some other way.The information describing one or more modifications to the storagesystem (408) may be received, for example, via a user interface thatenables an admin or other user to select various configuration changes.

The example method depicted in FIG. 5 also includes predicting (504)updated performance load on the storage system (408) in dependence upona load model of a modified storage system and the predictedcharacteristics (416) of the one or more workloads (420, 422, 424).Predicting (504) updated performance load on the storage system (408)may be carried out, for example, by utilizing the predictedcharacteristics (416) of the one or more workloads (420, 422, 424) thatwill be supported by the storage system as inputs to a load model ofassociated with the storage system (408) as modified. In such a way, ifthe one or more workloads (420, 422, 424) do change over time aspredicted (thereby resulting in a change to the amount of systemresources that are consumed by the workloads), and the load model (412)can accurately predict how well the storage system (408) as modifiedcould support the workloads in their new state, the performance load onthe modified storage system (408) can be accurately predicted (504).Readers will appreciate that, as described above, a load model (412) maybe used that was developed for storage systems that most closelyresemble the storage system (408) as modified. As such, the load modelthat is used to predict (504) updated performance load on the storagesystem (408) may be different than the load model that was used topredict (418) performance load on the storage system (408) prior toreceiving (502) information describing one or more modifications to thestorage system (408).

Readers will appreciate that although the preceding paragraphs describereceiving (502) information describing one or more modifications to thestorage system (408), the information may actually describe one or morepossible modifications to the storage system (408)—rather than an actualmodification. As such, prior to making actual modifications to thestorage system (408), the impact of such modifications may be analyzedby predicting (504) updated performance load on the storage system (408)in dependence upon the predicted characteristics (416) of the one ormore workloads (420, 422, 424) and a load model for storage systems thatmost closely resemble the storage system if the one or more possiblemodifications to the storage system (408) were actually made. Suchinformation may be useful for evaluating whether to actually proceedwith the modifications to the storage system (408).

For further explanation, FIG. 6 sets forth a flowchart illustrating anadditional example method of workload planning in a storage systemaccording to some embodiments of the present disclosure. The examplemethod depicted in FIG. 6 is similar to the example methods describedabove, as the example method depicted in FIG. 6 also includes generating(410) a load model (412), generating (414) predicted characteristics(416) of the one or more workloads (420, 422, 424), and predicting (418)performance load on the storage system (408) in dependence upon the loadmodel (412) and the predicted characteristics (416) of the one or moreworkloads (420, 422, 424).

The example method depicted in FIG. 6 also includes receiving (602)information describing one or more workloads to be removed from thestorage system (408). The information describing one or more workloadsto be removed from the storage system (408) may be embodied, forexample, as an identifier of a particular workload that is to be removedfrom the storage system (408), as a generalized description (e.g.,Oracle database) of a workload to be removed from the storage system(408), as an identification of a volume that is used to support aworkload that is to be removed from the storage system (408), or in someother way. The information describing one or more workloads to beremoved from the storage system (408) may be received, for example, viaa user interface that enables an admin or other user to select workloadsto be removed from the storage system (408).

The example method depicted in FIG. 6 also includes predicting (604) anupdated performance load on the storage system (408) in dependence uponthe load model (412) and the predicted characteristics of remainingworkloads on the storage system (408). Predicting (604) an updatedperformance load on the storage system (408) in dependence upon the loadmodel (412) and the predicted characteristics of remaining workloads onthe storage system (408) may be carried out, for example, by utilizingthe predicted characteristics (416) of the one or more remainingworkloads on the storage system (408)—excluding the workload that willbe removed—as inputs to the load model (412) associated with the storagesystem (408). In such a way, if the one or more remaining workloads(420, 422, 424) do change over time as predicted (thereby resulting in achange to the amount of system resources that are consumed by theworkloads), and the load model (412) can accurately predict how well thestorage system (408) could support the workloads in their new state, theperformance load on the storage system (408) can be accurately predicted(418). For example, if a first workload (420) is to be removed from thestorage system (408), predicting (604) an updated performance load onthe storage system (408) in dependence upon the load model (412) and thepredicted characteristics of remaining workloads on the storage system(408) may be carried out by utilizing the predicted characteristics(416) of the remaining workloads (422, 424) on the storage system (408)as inputs to the load model (412) associated with the storage system(408).

Readers will appreciate that although the preceding paragraphs describereceiving (602) information describing one or more workloads to beremoved from the storage system (408), the information may actuallydescribe one or more workloads that are candidates for removal from thestorage system (408)—rather than workloads that have actually beenremoved from the storage system (408). As such, prior to actuallyremoving a workload from the storage system (408), the impact ofremoving the workload from the storage system (408) may be analyzed bypredicting (604) an updated performance load on the storage system (408)in dependence upon the load model (412) and the predictedcharacteristics of proposed remaining workloads on the storage system(408). Such information may be useful for evaluating whether to actuallyproceed with removing the workload from the storage system (408).

For further explanation, FIG. 7 sets forth a flowchart illustrating anadditional example method of workload planning in a storage systemaccording to some embodiments of the present disclosure. The examplemethod depicted in FIG. 7 is similar to the example methods describedabove, as the example method depicted in FIG. 7 also includes generating(410) a load model (412), generating (414) predicted characteristics(416) of the one or more workloads (420, 422, 424), and predicting (418)performance load on the storage system (408) in dependence upon the loadmodel (412) and the predicted characteristics (416) of the one or moreworkloads (420, 422, 424).

The example method depicted in FIG. 7 also includes receiving (702)information describing one or more workloads to be added to the storagesystem (408). The information describing one or more workloads to beadded to the storage system (408) may be embodied, for example, as anidentifier of a particular workload that is executing on another storagesystem that may be added to the storage system (408), as a generalizeddescription (e.g., Oracle database) of a workload to be added to thestorage system (408), as an identification of a volume on anotherstorage system that is used to support the workload that is to be addedto the storage system (408), or in some other way. The informationdescribing one or more workloads to be added to the storage system (408)may be received, for example, via a user interface that enables an adminor other user to select workloads to be added to the storage system(408).

The example method depicted in FIG. 7 also includes predicting (704) anupdated performance load on the storage system (408) in dependence uponthe load model (412) and the predicted characteristics of an updated setof workloads on the storage system (408). Predicting (704) an updatedperformance load on the storage system (408) in dependence upon the loadmodel (412) and the predicted characteristics of an updated set ofworkloads on the storage system (408) may be carried out, for example,by utilizing the predicted characteristics (416) of the workloads (420,422, 424) that were already supported by the storage system (408), aswell as predicted characteristics (416) of all workloads to be added tothe storage system (408), as inputs to the load model (412) associatedwith the storage system (408). In such a way, if the workloads (420,422, 424) that were already supported by the storage system (408) andthe workloads to be added to the storage system (408) do change overtime as predicted (thereby resulting in a change to the amount of systemresources that are consumed by the workloads), and the load model (412)can accurately predict how well the storage system (408) could supportthe workloads in their new state and the performance load on the storagesystem (408) can be accurately predicted (418). For example, if aworkload (706) from a first storage system (402) and a workload (708)from a second storage system (406) are to be added to the storage system(408), predicting (704) an updated performance load on the storagesystem (408) in dependence upon the load model (412) and the predictedcharacteristics of an updated set of workloads on the storage system(408) may be carried out by utilizing the predicted characteristics(416) of the workloads (420, 422, 424) already supported by the storagesystem (408) as well as the predicted characteristics of the workloads(706, 708) to be added to the storage system (408) as inputs to the loadmodel (412) associated with the storage system (408).

Readers will appreciate that although the preceding paragraphs describereceiving (702) information describing one or more workloads to be addedto the storage system (408), the information may actually describe oneor more workloads that are candidates for addition to the storage system(408)—rather than workloads that have actually added to storage system(408). As such, prior to actually adding a workload to the storagesystem (408), the impact of adding the workload to the storage system(408) may be analyzed by predicting (704) an updated performance load onthe storage system (408) in dependence upon the load model (412) and thepredicted characteristics of an updated set of workloads on the storagesystem (408). Such information may be useful for evaluating whether toactually proceed with adding the workloads to the storage system (408).

Readers will further appreciate that although the steps of receiving(602) information describing one or more workloads to be removed fromthe storage system (408) and predicting (604) an updated performanceload on the storage system (408) in dependence upon the load model (412)and the predicted characteristics of remaining workloads on the storagesystem (408) are described with reference to the embodiment depicted inFIG. 6, while the steps of receiving (702) information describing one ormore workloads to be added to the storage system (408) and predicting(704) an updated performance load on the storage system (408) independence upon the load model (412) and the predicted characteristicsof an updated set of workloads on the storage system (408) are describedwith reference to the embodiment depicted in FIG. 7, embodiments of thepresent disclosure can include a combination of all these steps.Consider an example in which two workloads (706, 708) from other storagesystems (402, 406) are candidates for addition to the storage system(408) and that, as part of adding these workloads (706, 708), workloads(420, 422) already supported by the storage system (408) would bemigrated to other storage systems (402, 406). In such an example, theimpact of adding workloads and removing workloads could be evaluated incombination, by passing predicted characteristics associated the updatedset of workloads (424, 706, 708) that could be supported by the storagesystem (408) as inputs to the load model (412) associated with thestorage system (408). Such embodiments may be useful for identifying anoptimal set of workloads that could be placed on the storage system(408). In order to do so, it may also be necessary to generate predictedcharacteristics of workloads that may be added to the storage system inthe same manner as described above in step 414.

For further explanation, FIG. 8 sets forth a flowchart illustrating anadditional example method of workload planning in a storage systemaccording to some embodiments of the present disclosure. The examplemethod depicted in FIG. 8 is similar to the example methods describedabove, as the example method depicted in FIG. 8 also includes generating(410) a load model (412), generating (414) predicted characteristics(416) of the one or more workloads (420, 422, 424), and predicting (418)performance load on the storage system (408) in dependence upon the loadmodel (412) and the predicted characteristics (416) of the one or moreworkloads (420, 422, 424).

In the example methods described herein, each of the one or more ofworkloads may be defined by one or more volumes on the storage system.In such an example, a workload may be defined by one or more volumes onthe storage system in the sense that servicing I/O operations directedto a particular volume is a workload that a storage system must support.In such an example, because the storage system may maintain and trackinformation such as, for example, the amount of space consumed by thevolume, the amount of performance resources consumed to support theservicing of I/O operations to the volume, such information may beuseful in determining the amount of load placed on the storage system asa result of servicing the workload. In many cases, a particular workloadmay cause I/O operations to be directed to a single volume, but in othercases a workload may cause I/O operations to be directed to multiplevolumes. In either case, information associated with the volumes may begathered (and combined if appropriate) to determine the amount loadplaced on the storage system as a result of servicing the workload.

The example method depicted in FIG. 8 also includes determining (802)when the predicted performance load on the storage system (408) willexceed performance capacity of the storage system (408). The performancecapacity of the storage system (408) may be expressed, for example, asthe number of IOPS that the storage system (408) can service, the amountof network bandwidth available within the storage system (408), theamount of read bandwidth that can be serviced by the storage system(408), the amount of write bandwidth that can be serviced by the storagesystem (408), the amount of various storage resources (e.g., NVRAM)within the storage system (408), and many others. In fact, theperformance capacity of the storage system (408) may even be calculatedas a function of a combination of such characteristics of the storagesystem (408) in a way that in analogous to the manner in whichperformance load on the storage system (408) is determined. As such, theperformance capacity of the storage system (408) may be expressed inunits of measure that are identical to performance load, such that theperformance capacity of the storage system (408) may be expressed as amaximum performance load that can be supported by the storage system(408).

The example method depicted in FIG. 8 also includes generating (804), independence upon predicted performance load on the storage system (408),a recommendation. The recommendation that is generated (804) mayinclude, for example, a recommendation to perform a hardware or softwareupgrade on the storage system (804), a recommendation to move a workloadfrom the storage system (408) to another storage system, and so on. Insuch an example, rules may be in place such that recommendations aregenerated (804), for example, when the predicted performance load on thestorage system (408) reaches a predetermined threshold, when thepredicted performance load on the storage system (408) is expected toexceed performance capacity of the storage system (408) within apredetermined period of time, when additional storage systems are addedto or removed from a cluster, when other storage systems within acluster are modified (e.g., a hardware or software updated occurs), whenthe storage system (408) itself is modified, and so on. In such anexample, the recommendation may be presented (e.g., via a GUI, via amessage) to a system administrator or other user that can take action inresponse to the recommendation. Likewise, the recommendation may be sentto an upgrade module or other automated module that may carry out therecommended course of action (e.g., installing a software patch,migrating a workload).

Readers will appreciate that although the embodiments described aboverelate to embodiments where steps (i.e., the steps in the examplemethods described above) are performed by a storage system, otherembodiments are within the scope of the present disclosure. In fact, thesteps described above may also be carried out, for example, by aworkload planning module that is outside of the storage systems. Such aworkload planning module may be embodied, for example, as a module ofcomputer program instructions executing on computer hardware within acomputing device (e.g., a server) that is external to the storagesystems described above. In such an embodiment, the computing devicethat is external to the storage systems described above may be coupledto the storage systems via one or more data communications networks,data communications links, or otherwise configured to engage in datacommunications directly or indirectly with the storage systems. In suchan example, messages may be exchanged between the computing device thatis external to the storage systems and the storage systems themselves.In an additional embodiment, the steps described above may also becarried out, for example, by a workload planning module that is embodiedas a module of computer program instructions executing on computerhardware within a cloud environment. In such an embodiment, the workloadplanning module may be communicatively coupled to the storage systemsvia one or more data communications networks, data communications links,or otherwise configured to engage in data communications directly orindirectly with the storage systems. In such an example, messages may beexchanged between the workload planning module and the storage systemsthemselves. For completeness, FIG. 9 sets forth an embodiment in whichan example method is carried out by a workload planning module (908)that is external to any of the storage systems, although the methoddepicted in FIG. 9 could also be carried out by one or more of thestorage systems (either alone or in combination with the workloadplanning module).

For further explanation, FIG. 9 sets forth a flowchart illustrating anadditional example method of workload planning in a storage systemaccording to some embodiments of the present disclosure. The examplemethod depicted in FIG. 9 is similar to the example methods describedabove, as the example method depicted in FIG. 9 also includes many ofthe same or related steps.

The example method depicted in FIG. 9 includes generating (910), independence upon data (404) collected from a plurality of storage systems(402, 406, 408), one or more load models (912) that predicts performanceload on one or more storage systems (406, 408) in a fleet (906) ofstorage systems (406, 408) based on characteristics of each workload(422, 424, 904) supported by the fleet (906) of storage systems (406,408). The fleet (906) of storage systems (406, 408) depicted in FIG. 9may be embodied, for example, as a plurality of storage systems (406,408) that belong to a single business entity, as a plurality of storagesystems (406, 408) that are managed under a single management plane, andso on.

The data (404) collected from a plurality of storage systems (402, 406,408) may be embodied, for example, as telemetry data that isperiodically sent from the storage systems (402, 406, 408) to theworkload planning module (908). Such telemetry data may includeinformation that is useful for monitoring the operation of the storagesystem that sends the data including, for example, informationdescribing various performance characteristics of the storage system,information describing various workloads that are executing on thestorage system, and other types of information. The informationdescribing various performance characteristics of the storage system caninclude, for example, the number of TOPS being serviced by the storagesystem, the utilization rates of various computing resources (e.g., CPUutilization) within the storage system, the utilization rates of variousnetworking resources (e.g., network bandwidth utilization) within thestorage system, the utilization rates of various storage resources(e.g., NVRAM utilization) within the storage system, and many others.Likewise, the information describing various workloads that areexecuting on the storage system can include, for example, informationdescribing the number of IOPS being generated by a particular workload,overwrite rates for I/O operations that are being generated by theworkload, the amount of read bandwidth that is being consumed by I/Ooperations generated by the workload, and many others. As such, anexamination of the telemetry data can reveal characteristics of theworkloads (420, 422, 424, 902, 904) supported by one or more of thestorage systems (402, 406, 408). The characteristics of the workloads(420, 422, 424, 902, 904) executing on the storage systems (402, 406,408) can include, for example, information describing the number of TOPSbeing generated by the workload, overwrite rates for I/O operations thatare being generated by the workload, the amount of read bandwidth thatis being consumed by I/O operations generated by the workload, and manyothers.

In the example method depicted in FIG. 9, one or more load models (912)that predict performance load on one or more storage systems (406, 408)in a fleet (906) of storage systems (406, 408) may be generated (910).The term ‘performance load’ used herein may refer to a measure of loadon a storage system that is generated in dependence upon multiple systemmetrics. For example, the performance load on the storage systems (406,408) in the fleet (906) may be generated in dependence upon the amountof read bandwidth being serviced by the storage system, the amount ofwrite bandwidth being serviced by the storage system, the amount of IOPSbeing serviced by the storage system, the amount of computing load beingplaced on the storage system, the amount of data transfer load beingplaced on the storage system, and many other factors. In such anexample, the performance load on the storage systems (406, 408) in thefleet (906) may be calculated according to some formula that takes asinputs the weighted or unweighted combination of such factors describedin the preceding sentence. The performance load on the storage systems(406, 408) in the fleet (906) can therefore, in some embodiments,represent a single measure of load on a storage system that is generatedin dependence upon multiple system metrics.

In the example method depicted in FIG. 9, generating (910), independence upon data (404) collected from a plurality of storage systems(402, 406, 408), one or more load models (912) that predicts performanceload on one or more storage systems (406, 408) in a fleet (906) ofstorage systems (406, 408) based on characteristics of each workload(422, 424, 904) supported by the fleet (906) of storage systems (406,408) may be carried out, for example, through the use of machinelearning techniques. In such an example, machine learning algorithms maybe fed with information describing various performance characteristicsof various storage systems (as extracted from the telemetry data) andinformation describing various workloads that are executing on variousstorage systems (as extracted from the telemetry data) to identifycorrelations between the amount of performance load that was placed on aparticular storage system given the characteristics of the workloadsthat were executing on the particular storage system at the same pointin time. In such an example, one or more load models (412) may becreated for a variety of different storage system configurations. Forexample, load models may be created for storage systems that havedifferent hardware configurations, load models may be created forstorage systems that have different software configurations, load modelsmay be created for storage systems that have different configurationsettings, or any combination thereof. As such, each particular loadmodel that is generated may be specific to a particular combination ofhardware, software, configuration settings, or other attributes of aparticular storage system configuration. In other embodiments, eachparticular load model may be to a subset of such attributes of aparticular storage system configuration.

The example method depicted in FIG. 9 also includes generating (914),for each workload (422, 424, 904) supported by the fleet (906) ofstorage systems (406, 408), predicted characteristics (916) of theworkload (422, 424, 904). In the example method depicted in FIG. 4,generating (914) predicted characteristics (916) of each workload (422,424, 904) supported by the fleet (906) of storage systems (406, 408) caninclude performing a time-series analysis of each workload (422, 424,904). Readers will appreciate that the telemetry data described hereinmay not only be useful for enabling a centralized management service tomonitor the operation of the storage system that sends the data, butsuch telemetry data may also be useful for identifying trends associatedwith the workloads themselves. As such, an examination of the telemetrydata can be used to generate trending information for the workloadsincluding, for example, information describing the rate at which thenumber of IOPS being generated by the workload has been changing, therate at which overwrite rates for I/O operations that are beinggenerated by the workload are changing, the rate at which the amount ofread bandwidth that is being consumed by I/O operations generated by theworkload is changing, and many others. In such a way, predictedcharacteristics (416) of the one or more workloads (422, 424, 904) maybe generated (914) by extrapolating identified trends out over a periodof time in the future.

Consider an example in which telemetry data gathered from a plurality ofstorage systems (402, 404, 406) indicates that, on average, the amountof CPU resources required to support a virtual desktop infrastructureworkload doubles every three years. In such an example, if a particularworkload (422) that is executing on one storage system (408) in thefleet (906) is a virtual desktop infrastructure workload, generating(914) predicted characteristics (416) of such a workload may be carriedout, at least in part, by determining the amount of CPU resourcescurrently required to support the particular workload (422) and assumingthat the amount of CPU resources that will be required to support theparticular workload (422) in the future will double every three years.In such a way, the load demands created by each workload may beprojected to some point in the future.

The example method depicted in FIG. 9 also includes predicting (918)performance load on each storage system (406, 408) in the fleet (906) ofstorage systems (406, 408) in dependence upon the one or more loadmodels (912) and the predicted characteristics (916) of the one or moreworkloads (422, 424, 904). Predicting (918) performance load on eachstorage system (406, 408) in the fleet (906) of storage systems (406,408) may be carried out, for example, by utilizing the predictedcharacteristics (416) of the one or more workloads (422, 424, 904) thatwill be supported by a particular storage system as inputs to the loadmodel (912) associated with the particular storage system. In such away, if the one or more workloads (422, 424, 904) do change over time aspredicted (thereby resulting in a change to the amount of systemresources that are consumed by the workloads), and the load model (912)can accurately predict how well the particular storage system couldsupport the workloads in their new state, the performance load on thestorage system can be accurately predicted.

The example method depicted in FIG. 9 also includes identifying (920),for each storage system (406, 408) in the fleet (906) of storage systems(406, 408), a preferred placement for each of the one or more workloads(422, 424, 904). In the example method depicted in FIG. 9, identifying(920) a preferred placement for each of the one or more workloads (422,424, 904) may be carried out by identifying the one or more storagesystems (406, 408) in the fleet (906) of storage systems (406, 408) thatshould be used to support a particular workload (422, 424, 904).Identifying (920) a preferred placement for each of the one or moreworkloads (422, 424, 904) may be carried out, for example, by predicting(918) the performance load on each storage system (406, 408) in thefleet (906) of storage systems (406, 408) for each possible permutationthat workloads can be distributed across the storage systems (406, 408)in the fleet (906). In such an example, various criteria could beutilized to identify which permutation would represent a best fit. Forexample, in one embodiment the best fit could be identified as thepermutation that resulted in the longest period of time until thepredicted performance load on any of the storage systems (406, 408) willexceed performance capacity of the particular storage system.Alternatively, in another embodiment the best fit could be identified asthe permutation that resulted in the largest amount of differencebetween: 1) the performance capacity of the storage system, and 2) thepredicted performance load on the storage system at a predetermined time(e.g., in 2 months) of the fullest (in terms of performance load v.performance capacity) storage system in the fleet (906). In such a way,by identifying (920) a preferred placement for each of the one or moreworkloads (422, 424, 904), a fleet-level view can be taken and workloadssupported by the fleet (906) of storage systems (406, 408) can bedistributed in a way that is optimal for the fleet (906) as a whole.

The example method depicted in FIG. 9 includes migrating (922), independence upon the preferred placement for each of the one or moreworkloads (422, 424, 904), a particular workload (422, 424, 904) amongthe storage systems (406, 408) in the fleet (906) of storage systems(406, 408). In the example method depicted in FIG. 9, migrating (922) aparticular workload (422, 424, 904) among the storage systems (406, 408)in the fleet (906) of storage systems (406, 408) in dependence upon thepreferred placement for each of the one or more workloads (422, 424,904) can result in a particular workload (422, 424, 904) being movedfrom one storage system within the fleet (906) to another storage systemwithin the fleet (906). For example, if identifying (920) a preferredplacement for a first workload (422) revealed that the preferredplacement for the first workload (422) was on storage system (408), thefirst workload (422) could be migrated (922) from storage system (406)to storage system (408). In such an example, migrating (922) aparticular workload could cause the data contained in a particularvolume (or any other data that is specific to the workload beingmigrated) to be moved from one storage system to another storage system.Data may be moved, for example, using asynchronous or synchronous datareplication techniques, by sending the data from one storage system toanother storage system via a data communications path between thestorage systems, or in some other way. Readers will appreciate thatmigrating (922) a particular workload (422, 424, 904) among the storagesystems (406, 408) in the fleet (906) of storage systems (406, 408) independence upon the preferred placement for each of the one or moreworkloads (422, 424, 904) may be carried out automatically and withoutuser intervention. For example, the state of the fleet (906) may beconstantly monitored and workloads may be automatically migrated as thestate of the fleet (906) changes (e.g., workloads are added, workloadsare deleted, storage systems are added to the fleet, storage systems areremoved from the fleet, storage systems within the fleet are modified,devices within a particular storage system fail, and so on).

Readers will appreciate that embodiments of the present disclosure canimprove the operation of individual storage systems as well as fleets ofstorage systems. In fact, the embodiments described above can result inthe intelligent placement of workloads within individual storage systemsand across a fleet of storage systems, as the performance capabilitiesof each storage system may be intelligently aligned with the performanceload that is generated by each workload and by a collection ofworkloads. In such a way, system resources may be efficiently utilizedwithout overwhelming individual storage systems. Furthermore, actions(e.g., an automated software upgrade) may be initiated upon thedetection of a possible overconsumption of performance resources, suchthat the storage systems can experience less downtime as problems may beresolved before the problems actually occur, rather than reactivelyaddressing problems as they arise.

In some embodiments of the present disclosure, when a particularworkload (or volume) is moved from a first storage system to a secondstorage system, the amount of performance capacity and even storagecapacity that will become available on the first storage system may bedetermined, presented to a user such as a system admin, or otherwiseutilized. One issue, however, is that moving a particular workload (orvolume) may require that snapshots of volume or snapshots otherwiseassociated with the workload may also need to be moved to the secondstorage system. Because any of this data (e.g., the data in the volumeor portions of the snapshot) may be deduplicated, however, it may bechallenging to ascertain the size of the particular volume and itsrelated snapshots, as moving some data may not actually cause that datato be removed from the first storage system. Consider an example inwhich a particular block of data is written to a first volume on a firststorage system, the particular block of data is written to a secondvolume on the first storage system, and a deduplication processultimately deletes one copy of the block of data from the first storagesystem, such that the first volume and the second volume point to thesingle copy of the block of data that resides on the first storagesystem. If the first volume is subsequently moved to a second storagesystem, the block of data does not get deleted from the first storagesystem as the second volume still requires that a copy of that datablock be retained by the first storage system. As such, when predictinghow much storage capacity will be gained on the first storage system asa result of moving the first volume to another storage system, theamount of space that will be gained may be expressed using upper andlower bounds to effectively form a range. In some embodiments, the upperbound could be equal to the sum of the provisioned size of the volumeand the snapshots provisioned space, while the lower bound could beequal to the sum of the volume's unique space (i.e., volume data that isnot common with the data from other volumes or snapshots) and thesnapshot's unique space. Readers will appreciate that other formulas maybe used to calculate the upper and lower bounds.

Embodiments of the present disclosure can also include GUIs that may beused to provide and interact with the functionality described above. TheGUIs may be configured to illustrate the impact on a single storagesystem or a fleet of storage system if a particular action were taken,including deleting a volume, migrating a volume, or scaling the volumeover time. The GUI may be configured to display a list of workloads thatare supported by each of the storage system, where a user can select oneof the display workloads and select various actions to take. Forexample, a user could select the action of “migrate” where the GUI wouldthen present a list of storage systems that the user could migrate theselected workload to. In fact, available target storage systems may bepresented in one way (e.g., normal text) whereas unavailable targetstorage systems may be presented in another way (e.g., greyed out text).A particular storage system may be unavailable as a migration target,for example, because the storage system does not have sufficientcapacity to support the workload, because the storage system has beendesignated as being unavailable, or for other reasons. Likewise, the GUIcould support actions such as “copy” (where a copy of a workload iscreated on a second storage system), “delete”, or many others. In fact,the GUI could support a “simulation” function where performing someother action (e.g., migrate, copy, delete) is simulated in the sensethat the impact on each storage system is displayed so that a user candetermine whether to actually perform the action. The impact on eachstorage system could include, for example, displaying the projectedimpact on available storage capacity in each storage system that wouldresult from performing the action, displaying the predict performanceload on each storage that would result from performing the action, andso on. Such a simulate function could also be used to display the impacton one or more storage systems that would result from modifying any ofthe storage systems (e.g., adding additional storage devices, performinga software upgrade), including illustrating how workloads would bemigrated if the modification were to actually take place.

Embodiments of the present disclosure may also take into considerationwhether one or more volumes on a particular storage system are beingreplicated when determining where workloads should reside. In such anexample, if a particular volume is being replicated, recovery pointobjectives may be taken into account and workloads may be placed on thestorage system only if the storage system can still meet its recoverypoint objectives. For example, a particular workload may not be placedon a particular storage system if the storage system does not have theperformance capacity to support the workload and maintain recovery pointobjectives for replicated volumes on the storage system. As such,embodiments of the present disclosure may use machine learning todevelop a model to predict if a given protection group with meet itsRPO. Furthermore, modifications may be made to peak performance modelsand sustained performance models to take replication features intoaccount for arrays that replicate datasets.

For further explanation, FIG. 10 sets forth a flowchart illustrating anexample method of migrating workloads (1016) between a plurality ofexecution environments (1012 a, 1012 b, 1012 n) according to someembodiments of the present disclosure. In the example method depicted inFIG. 10, each of the execution environments (1012 a, 1012 b, 1012 n) maybe embodied as a collection of computer hardware resources and computersoftware resources that are capable of supporting the execution of aparticular workload (1016). The execution environments (1012 a, 1012 b,1012 n) may be embodied, for example, as a one or more of the storagesystems described above, as a cloud computing environment (including apublic cloud, a private cloud, or some combination thereof), as acollection of one or more servers, as a hyper-converged infrastructure(‘HCI’), as a converged infrastructure (‘CI’), as an infrastructure thatincludes a combination of HCI elements and CI elements, and so on.

The example method depicted in FIG. 10 includes identifying (1004), independence upon on characteristics (1002) of a workload (1016), one ormore execution environments (1012 a, 1012 b, 1012 n) that can supportthe workload (1016). Identifying (1004) one or more executionenvironments (1012 a, 1012 b, 1012 n) that can support the workload(1016) may be carried out, for example, by identifying characteristics(1002) of the workload (1016) and identifying the execution environments(1012 a, 1012 b, 1012 n) whose resources are capable of supporting theexecution of the workload (1016). For example, if the characteristics(1002) of the workload (1016) indicate that the workload (1016) usesblock storage, only those execution environments (1012 a, 1012 b, 1012n) that offer block storage may be identified (1004) as being anexecution environment (1012 a, 1012 b, 1012 n) that can support theworkload (1016). Alternatively, if the characteristics (1002) of theworkload (1016) indicate that the workload (1016) reads and writes fromfiles, only those execution environments (1012 a, 1012 b, 1012 n) thatoffer a file system may be identified (1004) as being an executionenvironment (1012 a, 1012 b, 1012 n) that can support the workload(1016). Readers will appreciate that many characteristics (1002) of theworkload (1016) may be taken into consideration when identifying (1004)one or more execution environments (1012 a, 1012 b, 1012 n) that cansupport the workload (1016). For example, characteristics (1002)describing the performance requirements of the workload (1016) may betaken into consideration, characteristics (1002) describing theavailability requirements of the workload (1016) may be taken intoconsideration, characteristics (1002) describing the data securityrequirements of the workload (1016) may be taken into consideration,characteristics (1002) describing the data resiliency requirements ofthe workload (1016) may be taken into consideration, and so on includingany combination of a wide range of characteristics.

The example method depicted in FIG. 10 also includes determining (1006),for each execution environment (1012 a, 1012 b, 1012 n), costs (1008)associated with supporting the workload (1016) on the executionenvironment (1012 a, 1012 b, 1012 n). Determining (1006) the costs(1008) associated with supporting the workload (1016) on each executionenvironment (1012 a, 1012 b, 1012 n) that was identified (1004) as beingcapable of supporting the workload (1016) may be carried out, forexample, by summing the financial costs that would be associated withsupporting the workload (1016) on each execution environment (1012 a,1012 b, 1012 n) that was identified (1004) as being capable ofsupporting the workload (1016).

Consider an example in which the execution environment (1012 a, 1012 b,1012 n) that was identified (1004) as being capable of supporting theworkload (1016) was a public cloud. In such an example, the costs (1008)associated with supporting the workload (1016) on the public cloud mayinclude the sum of: 1) the costs associated with storing a datasetaccessed by the workload in storage that provides sufficient performancefor the workload, 2) the costs associated with utilizing cloud-basedprocessing resources (including VMs, lambdas, and so on) that canexecute the workload in accordance with performance requirements of theworkload, 3) the costs associated with deploying the workload in a way(e.g., across multiple availability zones) so as to maintain requiredresiliency standards associated with the workload and its data, 4) andany other costs associated with deploying the workload in a publiccloud. Alternatively, if the execution environment (1012 a, 1012 b, 1012n) that was identified (1004) as being capable of supporting theworkload (1016) was an on-premises storage system, the costs (1008)associated with supporting the workload (1016) may be quite different.In such an example, the costs (1008) associated with supporting theworkload (1016) on an on-promises storage system may include the sumof: 1) the costs associated with purchasing the storage system, 2) thecosts associated with maintaining the storage system, 3) the costsassociated with deploying the storage system, 4) and any other costsassociated with deploying the workload in an on-premises storage system.In such an example, the costs (1008) associated with supporting theworkload (1016) on the execution environment (1012 a, 1012 b, 1012 n)may be expressed in terms of total dollars over the lifetime of theworkload, dollars per unit of time, or in some other way.

The example method depicted in FIG. 10 also includes selecting (1010),in dependence upon the costs (1008) associated with supporting theworkload (1016) on each the execution environments (1012 a, 1012 b, 1012n), a target execution environment for supporting the workload (1016).Selecting (1010) a target execution environment for supporting theworkload (1016) in dependence upon the costs (1008) associated withsupporting the workload (1016) on each the execution environments (1012a, 1012 b, 1012 n) may be carried out, for example, by selecting theexecution environment that can support the workload (1016) at the lowestcost as the target execution environment for supporting the workload(1016). In the example depicted in FIG. 10, a first executionenvironment (1012 a) is depicted as being the target executionenvironment that was selected (1010) for supporting the workload (1016).

The example method depicted in FIG. 10 also includes executing (1014)the workload (1016) on the target execution environment (1012 a).Executing (1014) the workload (1016) on the target execution environment(1012 a) may be carried out, for example, by deploying source codeassociated with the workload within a container in the target executionenvironment, by installing and executing an executable version of thesource code associated with the workload within the target executionenvironment, and in other ways. Readers will appreciate that in someembodiments, executing (1014) the workload (1016) on the targetexecution environment (1012 a) may be carried out by issuing a requestto the execution environment to execute a particular workload, as theentity that identifies (1004) one or more execution environments (1012a, 1012 b, 1012 n) that can support the workload (1016), determines(1006) costs (1008) associated with supporting the workload (1016) oneach execution environment (1012 a, 1012 b, 1012 n), and selects (1010)a target execution environment for supporting the workload (1016) mayonly be able to issue a request to the execution environment to executea particular workload. As such, for the purposes of this step and other‘executing’ steps described herein, executing (1014) the workload (1016)on the target execution environment (1012 a) and issuing a request tothe target execution environment (1012 a) to execute the workload (1016)can be viewed as being synonymous.

For further explanation, FIG. 11 sets forth a flowchart illustrating anadditional example method of migrating workloads between a plurality ofexecution environments according to some embodiments of the presentdisclosure. The example method depicted in FIG. 11 is similar to theexample method depicted in FIG. 10, as the example method depicted inFIG. 11 also includes identifying (1004) one or more executionenvironments (1012 a, 1012 b, 1012 n) that can support the workload(1016), determining (1006) costs (1008) associated with supporting theworkload (1016) on each execution environment (1012 a, 1012 b, 1012 n),selecting (1010) a target execution environment for supporting theworkload (1016), and executing (1014) the workload (1016) on the targetexecution environment (1012 a).

The example method depicted in FIG. 11 also includes detecting (1102) achange to the workload. Readers will appreciate that the workload maychange for a variety of reasons. For example, if the workload is avirtual desktop infrastructure, the workload may change in response to achange in the number of virtual desktops that are supported by thevirtual desktop infrastructure. Likewise, if the workload is a webportal for an online retailer, the workload may change in response to asuccessful advertising campaign substantially increasing the number ofcustomers that shop with the online retailer. In such an example,detecting (1102) a change to the workload may be carried out, forexample, by detecting a change in I/O operations issued by the workloadthat meets a predetermined threshold (e.g., 10% increase or decrease),by detecting a change in the amount of user-requests received by theworkload that meets a predetermined threshold (e.g., 10% increase ordecrease), by detecting a changes to a service level agreement (e.g.,via detecting a change to workload configuration information or othermetadata) such that the workload is required to provide a differentservice level to users of the workload, by detecting that the amount ofdata storage required by the workload has changed by a predeterminedthreshold, or in many other ways. In the example depicted in FIG. 11,the changed workload is illustrated by denoting the original workload asworkload (1016 a) whereas the changed workload is labelled as workload(1016 b).

Readers will appreciate that in response to detecting (1102) a change tothe workload, the steps of identifying (1004) one or more executionenvironments (1012 a, 1012 b, 1012 n) that can support the changedworkload (1016 b), determining (1006) costs (1008) associated withsupporting the changed workload (1016 b) on each execution environment(1012 a, 1012 b, 1012 n), and selecting (1010) a target executionenvironment for supporting the changed workload (1016 b) may beperformed to identify the optimal execution environment (1012 a, 1012 b,1012 n) for the changed workload (1016 b), as it may not be optimal toexecute the changed workload (1016 b) in the same execution environment(1012 a) as the workload (1012 a) in its original form. As such, a newtarget execution environment (1012 b) may be selected (1010) to supportthe execution of the changed workload (1016 b).

The example method depicted in FIG. 11 also includes migrating (1104)the workload from the target execution environment (1012 a) to a newtarget execution environment (1012 b). Migrating (1104) the workloadfrom the target execution environment (1012 a) to a new target executionenvironment (1012 b) may be carried out, for example, by destroying acontainer on the target execution environment (1012 a) that includessource code associated with the workload and deploying a container onthe new target execution environment (1012 b) that includes source codeassociated with the workload, by uninstalling and ceasing execution anexecutable version of the source code associated with the workload thetarget execution environment (1012 a) and also installing and executingan executable version of the source code associated with the workload onthe new target execution environment (1012 b), and in other ways.

For further explanation, FIG. 12 sets forth a flowchart illustrating anadditional example method of migrating workloads between a plurality ofexecution environments according to some embodiments of the presentdisclosure. The example method depicted in FIG. 12 is similar to theexample method depicted in FIG. 10, as the example method depicted inFIG. 12 also includes identifying (1004) one or more executionenvironments (1012 a, 1012 b, 1012 n) that can support the workload(1016), determining (1006) costs (1008) associated with supporting theworkload (1016) on each execution environment (1012 a, 1012 b, 1012 n),selecting (1010) a target execution environment for supporting theworkload (1016), and executing (1014) the workload (1016) on the targetexecution environment (1012 a).

The example method depicted in FIG. 12 also includes predicting (1202) achange to the workload. Readers will appreciate that the workload maychange for a variety of reasons. For example, if the workload is avirtual desktop infrastructure, the workload may change in response to achange in the number of virtual desktops that are supported by thevirtual desktop infrastructure. Likewise, if the workload is a webportal for an online retailer, the workload may change in response to asuccessful advertising campaign substantially increasing the number ofcustomers that shop with the online retailer. In such an example,predicting (1202) a change to the workload may be carried out, forexample, by detecting that the predicted performance load on aparticular execution environment will meet a predetermined threshold(e.g., 10% increase or decrease) as described above, through the use ofload models associated with a particular workload as described above,through the use of historical modeling, or in many other ways. In suchan example, predicting (1202) a change to the workload may vary fromdetecting (1102) a change to the workload, as described above, aschanges to the workload may not yet be fully realized at the time thatthe change was predicted (1202). In the example depicted in FIG. 12, thepredicted version of the workload is illustrated by denoting theoriginal workload as workload (1016 a) whereas the workload in itspredicted form is labelled as workload (1016 b).

Readers will appreciate that in response to predicting (1202) a changeto the workload, the steps of identifying (1004) one or more executionenvironments (1012 a, 1012 b, 1012 n) that can support the workload(1016 b) in its predicted form, determining (1006) costs (1008)associated with supporting the workload (1016 b) in its predicted formon each execution environment (1012 a, 1012 b, 1012 n), and selecting(1010) a target execution environment for supporting the workload (1016b) in its predicted form may be performed to identify the optimalexecution environment (1012 a, 1012 b, 1012 n) for the workload (1016 b)in its predicted form, as it may not be optimal to execute the workload(1016 b) in its predicted form in the same execution environment (1012a) as the workload (1012 a) in its original form. As such, a new targetexecution environment (1012 b) may be selected (1010) to support theexecution of the workload (1016 b) in its predicted form.

The example method depicted in FIG. 12 also includes migrating (1204)the workload from the target execution environment (1012 a) to a newtarget execution environment (1012 b). Migrating (1204) the workloadfrom the target execution environment (1012 a) to a new target executionenvironment (1012 b) may be carried out, for example, by destroying acontainer on the target execution environment (1012 a) that includessource code associated with the workload and deploying a container onthe new target execution environment (1012 b) that includes source codeassociated with the workload, by uninstalling and ceasing execution anexecutable version of the source code associated with the workload thetarget execution environment (1012 a) and also installing and executingan executable version of the source code associated with the workload onthe new target execution environment (1012 b), and in other ways.

For further explanation, FIG. 13 sets forth a flowchart illustrating anadditional example method of migrating workloads between a plurality ofexecution environments according to some embodiments of the presentdisclosure. The example method depicted in FIG. 13 is similar to theexample method depicted in FIG. 10, as the example method depicted inFIG. 13 also includes identifying (1004) one or more executionenvironments (1012 a, 1012 b, 1012 n) that can support the workload(1016), determining (1006) costs (1008) associated with supporting theworkload (1016) on each execution environment (1012 a, 1012 b, 1012 n),selecting (1010) a target execution environment for supporting theworkload (1016), and executing (1014) the workload (1016) on the targetexecution environment (1012 a).

The example method depicted in FIG. 13 also includes detecting (1302) achange to the costs (1008) associated with supporting the workload on aparticular execution environment (1012 a, 1012 b, 1012 n). Readers willappreciate that the costs (1008) associated with supporting the workloadon a particular execution environment (1012 a, 1012 b, 1012 n) maychange for a variety of reasons. Consider an example in which theexecution environment (1012 a, 1012 b, 1012 n) that was identified(1004) as being capable of supporting the workload (1016) was a publiccloud. In such an example, the costs (1008) associated with supportingthe workload (1016) on the public cloud may change because of a changein the pricing structure of the services offered by the public cloud,because of a change in the services offered by the public cloud, becauseof a change in the amount of services and resources consumed by theworkload, or for many other reasons. Alternatively, if the executionenvironment (1012 a, 1012 b, 1012 n) that was identified (1004) as beingcapable of supporting the workload (1016) was an on-premises storagesystem, the costs (1008) associated with supporting the workload (1016)may change due to a change in electricity prices at the facility thathouses the on-premises storage system, due to an upgrade of theon-premises storage system, due to a change in the amount of labor (orcost of labor) required to maintain the on-premises storage system, orfor many other reasons. In such an example, detecting (1302) a change tothe costs (1008) associated with supporting the workload on a particularexecution environment (1012 a, 1012 b, 1012 n) may be carried out inmany ways, including periodically re-performing the step of determining(1006) costs (1008) associated with supporting the workload (1016) oneach execution environment (1012 a, 1012 b, 1012 n).

Readers will appreciate that in response to detecting (1302) a change tothe costs (1008) associated with supporting the workload on a particularexecution environment (1012 a, 1012 b, 1012 n), the steps of identifying(1004) one or more execution environments (1012 a, 1012 b, 1012 n) thatcan support the workload (1016 b) in its predicted form, determining(1006) costs (1008) associated with supporting the workload (1016 b) inits predicted form on each execution environment (1012 a, 1012 b, 1012n), and selecting (1010) a target execution environment for supportingthe workload (1016 b) in its predicted form may be performed to identifythe optimal execution environment (1012 a, 1012 b, 1012 n) for theworkload (1016) given the updated costs associated with one or more ofthe execution environments (1012 a, 1012 b, 1012 n), as it may not beoptimal to execute the workload (1016 b) on the same executionenvironment that was selected given the original costs associated withone or more of the execution environments (1012 a, 1012 b, 1012 n). Assuch, a new target execution environment (1012 b) may be selected (1010)to support the execution of the workload (1016 b). In the exampledepicted in FIG. 13, the workload as originally deployed is illustratedby denoting the original workload as workload (1016 a) whereas theworkload as being redeployed in a new execution environment is labelledas workload (1016 b).

The example method depicted in FIG. 13 also includes migrating (1304)the workload from the target execution environment (1012 a) to a newtarget execution environment (1012 b). Migrating (1304) the workloadfrom the target execution environment (1012 a) to a new target executionenvironment (1012 b) may be carried out, for example, by destroying acontainer on the target execution environment (1012 a) that includessource code associated with the workload and deploying a container onthe new target execution environment (1012 b) that includes source codeassociated with the workload, by uninstalling and ceasing execution anexecutable version of the source code associated with the workload thetarget execution environment (1012 a) and also installing and executingan executable version of the source code associated with the workload onthe new target execution environment (1012 b), and in other ways.

For further explanation, FIG. 14 sets forth a flowchart illustrating anadditional example method of migrating workloads between a plurality ofexecution environments according to some embodiments of the presentdisclosure. The example method depicted in FIG. 14 is similar to theexample method depicted in FIG. 10, as the example method depicted inFIG. 14 also includes identifying (1004) one or more executionenvironments (1012 a, 1012 b, 1012 n) that can support the workload(1016), determining (1006) costs (1008) associated with supporting theworkload (1016) on each execution environment (1012 a, 1012 b, 1012 n),selecting (1010) a target execution environment for supporting theworkload (1016), and executing (1014) the workload (1016) on the targetexecution environment (1012 a).

In the example method depicted in FIG. 14, identifying (1004) one ormore execution environments (1012 a, 1012 b, 1012 n) that can supportthe workload (1016) can include identifying (1402) one or more executionenvironments (1012 a, 1012 b, 1012 n) that can meet predeterminedperformance requirements associated with the workload (1016). Thepredetermined performance requirements associated with the workload(1016) can include, for example, a number of IOPS generated by theworkload (1016) that should be supported, the read latency and writelatency that should be obtained for I/O operations generated by theworkload (1016), an amount of user requests that the workload should beable to respond to within a predetermined period of time, and manyothers. In such an example, only those that can meet predeterminedperformance requirements associated with the workload (1016) should beidentified (1004) as being execution environments (1012 a, 1012 b, 1012n) that can support the workload (1016).

For further explanation, FIG. 15 sets forth a flowchart illustrating anadditional example method of migrating workloads between a plurality ofexecution environments according to some embodiments of the presentdisclosure. The example method depicted in FIG. 15 is similar to theexample method depicted in FIG. 10, as the example method depicted inFIG. 15 also includes identifying (1004) one or more executionenvironments (1012 a, 1012 b, 1012 n) that can support the workload(1016), determining (1006) costs (1008) associated with supporting theworkload (1016) on each execution environment (1012 a, 1012 b, 1012 n),selecting (1010) a target execution environment for supporting theworkload (1016), and executing (1014) the workload (1016) on the targetexecution environment (1012 a).

In the example method depicted in FIG. 15, identifying (1004) one ormore execution environments (1012 a, 1012 b, 1012 n) that can supportthe workload (1016) can include identifying (1502), for a plurality ofworkloads (1016, 1510), one or more execution environments (1012 a, 1012b, 1012 n) that can support each workload (1016, 1510) in dependenceupon on characteristics (1002) of each workload (1016, 1510).Identifying (1502) one or more execution environments (1012 a, 1012 b,1012 n) that can support each workload (1016, 1510) may be carried out,for example, by identifying characteristics (1002) of each workload(1016, 1510) and identifying the execution environments (1012 a, 1012 b,1012 n) whose resources are capable of supporting the execution of eachof the workloads (1016, 1510). For example, if the characteristics(1002) of a first workload (1016) indicate that the workload (1016) usesblock storage, only those execution environments (1012 a, 1012 b, 1012n) that offer block storage may be identified (1502) as being anexecution environment (1012 a, 1012 b, 1012 n) that can support theworkload (1016). Alternatively, if the characteristics (1002) of asecond workload (1510) indicate that the workload (1510) reads andwrites from files, only those execution environments (1012 a, 1012 b,1012 n) that offer a file system may be identified (1502) as being anexecution environment (1012 a, 1012 b, 1012 n) that can support theworkload (1510). Readers will appreciate that many characteristics(1002) of each workload (1016, 1510) may be taken into considerationwhen identifying (1502) one or more execution environments (1012 a, 1012b, 1012 n) that can support each of the workloads (1016, 1510). Forexample, characteristics (1002) describing the performance requirementsof the each workload (1016, 1510) may be taken into consideration,characteristics (1002) describing the availability requirements of eachworkload (1016, 1510) may be taken into consideration, characteristics(1002) describing the data security requirements of each workload (1016,1510) may be taken into consideration, characteristics (1002) describingthe data resiliency requirements of each workload (1016, 1510) may betaken into consideration, and so on including any combination of a widerange of characteristics.

In the example method depicted in FIG. 15, determining (1006) costs(1008) associated with supporting the workload (1016) on each executionenvironment (1012 a, 1012 b, 1012 n) can include determining (1504), fora plurality of workload placement scenarios, cumulative costs associatedwith supporting each workload (1016, 1510) in accordance with each ofthe workload placement scenarios. In the example depicted in FIG. 15, aparticular workload placement scenario may include one permutation ofthe set of possible arrangements of the workloads (1016, 1510). Forexample, if a first execution environment (1012 a) and a secondexecution environment (1012 b) were identified (1502) as being capableof support a first workload (1016), whereas the second executionenvironment (1012 b) and a third execution environment (1012 n) wereidentified as being capable of supporting a second workload (1510), theset of possible arrangements of workloads would include: 1) the firstworkload (1016) is executed on the first execution environment (1012 a)and the second workload (1510) is executed on the second executionenvironment (1012 b), 2) the first workload (1016) is executed on thefirst execution environment (1012 a) and the second workload (1510) isexecuted on the third execution environment (1012 n), 3) the firstworkload (1016) is executed on the second execution environment (1012 b)and the second workload (1510) is also executed on the second executionenvironment (1012 b), and 4) the first workload (1016) is executed onthe second execution environment (1012 b) and the second workload (1510)is executed on the third execution environment (1012 n). In such anexample, each arrangement would be a distinct workload placementscenario.

Determining (1504) the cumulative costs associated with supporting eachworkload (1016, 1510) in accordance with each of the workload placementscenarios may be carried out, for example, by summing the financialcosts that would be associated with supporting each workload (1016,1510) on its respective execution environment (1012 a, 1012 b, 1012 n)as identified in the particular workload placement scenario. Continuingwith the example described above, the cumulative cost associated withworkload placement scenario would be as follows: 1) the sum of the costsassociated with executing the first workload (1016) on the firstexecution environment (1012 a) and the costs associated with executingthe second workload (1510) on the second execution environment (1012 b),2) the sum of the costs associated with executing the first workload(1016) on the first execution environment (1012 a) and costs associatedwith executing the second workload (1510) on the third executionenvironment (1012 n), 3) the sum of the costs associated with executingthe first workload (1016) on the second execution environment (1012 b)and the costs associated with executing the second workload (1510) onthe second execution environment (1012 b), and 4) the sum of the costsassociated with executing the first workload (1016) on the secondexecution environment (1012 b) and the costs associated with executingthe second workload (1510) on the third execution environment (1012 n).

Consider an example in which the execution environment (1012 a, 1012 b,1012 n) that was identified (1502) as being capable of supporting afirst workload (1016) was a public cloud. In such an example, the costs(1008) associated with supporting the workload (1016) on the publiccloud may include the sum of: 1) the costs associated with storing adataset accessed by the workload in storage that provides sufficientperformance for the workload, 2) the costs associated with utilizingcloud-based processing resources (including VMs, lambdas, and so on)that can execute the workload in accordance with performancerequirements of the workload, 3) the costs associated with deploying theworkload in a way (e.g., across multiple availability zones) so as tomaintain required resiliency standards associated with the workload andits data, 4) and any other costs associated with deploying the workloadin a public cloud. Likewise, if the execution environment (1012 a, 1012b, 1012 n) that was identified (1502) as being capable of supporting asecond workload (1510) was an on-premises storage system, the costs(1008) associated with supporting the workload (1510) may be quitedifferent. In such an example, the costs (1008) associated withsupporting the workload (1510) on an on-promises storage system mayinclude the sum of: 1) the costs associated with purchasing the storagesystem, 2) the costs associated with maintaining the storage system, 3)the costs associated with deploying the storage system, 4) and any othercosts associated with deploying the workload in an on-premises storagesystem. In such an example, the costs (1008) associated with supportingthe workload (1510) on the execution environment (1012 a, 1012 b, 1012n) may be expressed in terms of total dollars over the lifetime of theworkload, dollars per unit of time, or in some other way.

In the example method depicted in FIG. 15, selecting (1010) a targetexecution environment for supporting the workload (1016) can includeselecting (1506), in dependence upon the cumulative costs associatedwith supporting each workload (1016, 1510) in accordance with each ofthe workload placement scenarios, a target execution environment forsupporting each workload (1016, 1510). Selecting (1506) a targetexecution environment for supporting each workload (1016, 1510) independence upon the cumulative costs associated with supporting eachworkload (1016, 1510) in accordance with each of the workload placementscenarios may be carried out, for example, by selecting the workloadplacement scenario that had the lowest cumulative cost.

In the example method depicted in FIG. 15, executing (1014) the workload(1016) on the target execution environment (1012 a) can includeexecuting (1508 a, 1508 b) each workload (1016, 1510) on its selectedtarget execution environment (1012 a, 1012 b). Executing (1508 a, 1508b) each workload (1016, 1510) on its selected target executionenvironment (1012 a, 1012 b) may be carried out, for example, bydeploying source code associated with each workload within a containerin its respective target execution environment, by installing andexecuting an executable version of the source code associated with eachworkload within its respective target execution environment, and inother ways.

Readers will appreciate that although the example depicted in FIG. 15relates to an embodiment where two workloads (1016, 1510) are placedwithin one or more execution environments, in other embodiments, alarger number of workloads may be similarly placed within one or moreexecution environments. In fact, embodiments of the present disclosurecan include additional workloads and additional execution environments.

For further explanation, FIG. 16 sets forth a flowchart illustrating anadditional example method of migrating workloads between a plurality ofexecution environments according to some embodiments of the presentdisclosure. The example method depicted in FIG. 16 is similar to theexample method depicted in FIG. 10, as the example method depicted inFIG. 16 also includes identifying (1004) one or more executionenvironments (1012 a, 1012 b, 1012 n) that can support the workload(1016), determining (1006) costs (1008) associated with supporting theworkload (1016) on each execution environment (1012 a, 1012 b, 1012 n),selecting (1010) a target execution environment for supporting theworkload (1016), and executing (1014) the workload (1016) on the targetexecution environment (1012 a).

In the example method depicted in FIG. 16, identifying (1004) one ormore execution environments (1012 a, 1012 b, 1012 n) that can supportthe workload (1016) can include identifying (1602) one or more executionenvironments that can support the workload in dependence upon on aworkload fingerprint. In the example method depicted in FIG. 16, theworkload fingerprint may include information describing the currentcharacteristics of the workload as well as information describing theexpected future characteristics of the workload. For example, theworkload fingerprint may include information describing the number ofIOPS currently (or within a recent predetermined period of time (e.g.,within the last minute)) being generated by the workload, informationdescribing the rate at which the number of IOPS being generated by theworkload has been changing, the current (or within a recentpredetermined period of time (e.g., within the last minute)) overwriterate for I/O operations being generated by the workload, the rate atwhich overwrite rates for I/O operations that are being generated by theworkload are changing, the amount of read bandwidth currently (or withina recent predetermined period of time (e.g., within the last minute))being consumed by I/O operations generated by the workload, the rate atwhich the amount of read bandwidth that is being consumed by I/Ooperations generated by the workload is changing, and many others. Insuch a way, the workload fingerprint can include information that can beused to generate predicted characteristics of the one or more workloadsby extrapolating identified trends out over a period of time in thefuture. Identifying (1602) one or more execution environments that cansupport the workload in dependence upon on a workload fingerprint maytherefore be carried out, for example, by identifying those executionenvironments that could support the workload in its predicted state inthe future.

In the example method depicted in FIG. 16, determining (1006) costs(1008) associated with supporting the workload (1016) on each executionenvironment (1012 a, 1012 b, 1012 n) can include determining (1604), foreach execution environment, the costs associated with supporting theworkload in dependence upon on the workload fingerprint. Determining(1604), for each execution environment, the costs associated withsupporting the workload in dependence upon on the workload fingerprintmay be carried out, for example, by identifying the costs that would beassociated with supporting the workload on a particular executionenvironment if the workload were to change as predicted. In the examplemethod depicted in FIG. 16, the workload fingerprint may be created bygenerating, for one or more of workloads, predicted characteristics ofthe one or more workloads, as described in greater detail above. In suchan example, telemetry data may be utilized to generate predictedcharacteristics of the one or more workloads, as described in greaterdetail above.

For further explanation, FIG. 17 sets forth a flowchart illustrating anexample method of workload (1716) placement based on carbon emissionsaccording to some embodiments of the present disclosure. The examplemethod depicted in FIG. 17 includes calculating (1702), for eachexecution environment (1712 a, 1712 b, 1712 n) of a plurality ofexecution environments (1712 a, 1712 b, 1712 n), a carbon emission cost(1704) associated with a workload (1716). In the example method depictedin FIG. 17, each of the execution environments (1712 a, 1712 b, 1712 n)may be embodied as a collection of computer hardware resources andcomputer software resources that are capable of supporting the executionof a particular workload (1716). The execution environments (1712 a,1712 b, 1712 n) may be embodied, for example, as a one or more of thestorage systems described above, as a cloud computing environment(including a public cloud, a private cloud, or some combinationthereof), as a collection of one or more servers, as a hyper-convergedinfrastructure (‘EICI’), as a converged infrastructure (‘CI’), as aninfrastructure that includes a combination of HCI elements and CIelements, and so on.

The carbon emission cost (1704) for a particular execution environment(1712 a, 1712 b, 1712 n) is a quantitative expression of an amount ofcarbon emitted is association with the use of the particular executionenvironment (1712 a, 1712 b, 1712 n). For example, the carbon emissioncost (1704) may include an emission intensity calculated as an amount ofcarbon by weight (e.g., in grams) emitted per unit of energy (e.g., perjoule, per kilojoule, per kilowatt hour, and the like). Accordingly, thecarbon emission cost (1704) may be calculated as an average, median,minimum, maximum, or other aggregate emission intensity within aparticular time duration (e.g., based on historic or recorded emissionintensity values).

For example, assume that a particular execution environment (1712 a,1712 b, 1712 n) includes hardware resources located in a particulargeographic area (e.g., city, country, and the like). Further assume thatdata is accessible indicating the emission intensity for that geographicarea. For example, such data may be published by government entities,non-profits, regulatory groups, and the like. Such data may includeaggregate emission intensities for that area (e.g., the average emissionintensity for that area). Such data may also include particularsamplings of emission intensities for that area from which an aggregateintensity for that area may be calculated. In some embodiments, thecarbon emission cost (1704) may include a projected emission intensity.For example, the projected emission intensity may be projected based onhistoric emission intensity samplings using various projectiontechniques, including trend forecasting, machine learning models, andthe like.

In some embodiments, the carbon emission cost (1704) for a particularexecution environment (1712 a, 1712 b, 1712 n) may be expressed as aparticular amount of carbon by weight estimated to be emitted throughuse of the particular execution environment (1712 a, 1712 b, 1712 n).For example, assuming an emission intensity value for the particularexecution environment (1712 a, 1712 b, 1712 n) (e.g., calculated asdescribed above or determined by another approach), the amount of carbonfor the carbon emission cost (1704) may be calculated as a function ofthe emission intensity value and an estimated amount of energy to beused by the particular execution environment (1712 a, 1712 b, 1712 n).

The estimated amount of energy may include an estimated amount of energyto be used in executing the workload (1716) in the particular executionenvironment (1712 a, 1712 b, 1712 n). For example, an amount of energyto be used in executing the workload (1716) may be based on energy usagesamplings for historic executions of the workload (1716). The amount ofenergy to be used in executing the workload (1716) may also includeprojected energy usages in executing the workload (1716). For example,particular characteristics of the workload (1716) may be provided to amachine learning model trained to provide an estimated or projectedenergy usage for executing the workload (1716). Such characteristics mayinclude estimated numbers of Input/Output operations (e.g., IOPS), diskread or write operations, estimated processor usages, and the like. Inother words, the amount of energy used in executing the workload (1716),and therefore the carbon emission cost (1704) is calculated based on aprojected workload behavior. In other embodiments, the amount of used inexecuting the workload (1716) may be calculated independent ofparticular characteristics of the workload (1716) (e.g., based onhistoric, estimated, or projected energy uses of the particularexecution environment (1712 a, 1712 b, 1712 n) for any workload (1716)).

In some embodiments, the estimated amount of energy used in executingthe workload (1716) in the particular execution environment (1712 a,1712 b, 1712 n) may be calculated based on particular hardware resourcesor devices in the particular execution environment (1712 a, 1712 b, 1712n). As each execution environment (1712 a, 1712 b, 1712 n) may includedifferent hardware components that use different amounts of energy, theamount of energy used by a given execution environment (1712 a, 1712 b,1712 n) may vary due to these different hardware components. As anexample, an execution environment (1712 a, 1712 b, 1712 n) composed ofolder, less energy efficient hardware components may use more energy toexecute a given workload (1716) compared to another executionenvironment (1712 a, 1712 b, 1712 n) with newer, more efficient hardwarecomponents or devices. Accordingly, the estimated amount of energy usedin executing the workload (1716) in the particular execution environment(1712 a, 1712 b, 1712 n) may be calculated based on estimated or sampledenergy usages for particular hardware components in the executionenvironment (1712 a, 1712 b, 1712 n).

As an example, assume a projected emission intensity for a givenexecution environment (1712 a, 1712 b, 1712 n) for a particularprojected time window (e.g., six months, one year, and the like).Further assume a projected energy usage in executing the workload (1716)in the particular execution environment (1712 a, 1712 b, 1712 n) withinthat time window. The carbon emission cost (1704) for executing theworkload (1716) in the particular execution environment (1712 a, 1712 b,1712 n) may then be calculated as a function (e.g., a product or asanother function) of the projected emission intensity and the projectedenergy usage.

The example method depicted in FIG. 17 also includes selecting (1706),based on each carbon emission cost (1704) for the plurality of executionenvironments (1712 a, 1712 b, 1712 n), a target execution environment(1712 a). For example, in some embodiments, the target executionenvironment (1712 a) is selected (1706) as having a lowest carbonemission cost of the execution environments (1712 a, 1712 b, 1712 n). Asanother example, in some embodiments, the target execution environment(1712 a) is selected (1706) as having a lowest carbon emission costwhile satisfying one or more thresholds as will be described in moreretail below. As a further example, in some embodiments, the targetexecution environment (1712 a) is selected (1706) as a function of thecarbon emission cost (1704) and other factors as will be described inmore detail below. One skilled in the art will appreciate that thetarget execution environment (1712 a) may also be selected (1706) usingthe carbon emission cost (1704) by other approaches.

The example method depicted in FIG. 17 also includes executing (1708)the workload (1016) on the target execution environment (1712 a).Executing (1708) the workload (1716) on the target execution environment(1712 a) may be carried out, for example, by deploying source codeassociated with the workload (1716) within a container in the targetexecution environment (1712 a), by installing and executing anexecutable version of the source code associated with the workload(1716) within the target execution environment (1712 a), and in otherways. In some embodiments, executing (1708) the workload (1716) on thetarget execution environment (1712 a) may include migrating the workload(1716) from another execution environment (1712 a, 1712 b, 1712 n).

Readers will appreciate that in some embodiments, executing (1708) theworkload (1716) on the target execution environment (1712 a) may becarried out by issuing a request to the target execution environment(1712 a) to execute a particular workload (1716). As such, for thepurposes of this step and other ‘executing’ steps described herein,executing (1708) the workload (1716) on the target execution environment(1712 a) and issuing a request to the target execution environment (1712a) to execute the workload (1716) can be viewed as being synonymous.

In some embodiments, executing (1708) the workload (1716) is performedautomatically in response to selecting (1706) the target executionenvironment (1712 a). In other embodiments, executing (1708) theworkload (1716) is performed automatically in response to a request orconfirmation from a user or other entity to execute (1708) the workload(1716) on the selected target execution environment (1712 a).

Although the preceding discussion describes selecting (1706) a targetexecution environment (1712 a) for a single workload (1716), one skilledin the art will appreciate that the teachings described herein may beused in the context of multiple workloads (1716). As an example, theapproaches described herein may be used to identify a target executionenvironment for deploying multiple workloads (1716). Such a targetexecution environment may be identified as having a lowest carbonemission cost (e.g., for execution each of the workloads (1716)). Asanother example, the approaches described herein may be used to identifya particular arrangement or combination of execution environments (1712a, 1712 b, 1712 n). The particular arrangement of execution environments(1712 a, 1712 b, 1712 n) may be selected as a particular combination ofexecution environments (1712 a, 1712 b, 1712 n) and executed workloads(1716) having a lowest combined carbon emission cost (1704) across allexecution environments (1712 a, 1712 b, 1712 n), as a particularcombination of execution environments (1712 a, 1712 b, 1712 n)minimizing carbon emission costs (1704) while maximizing or minimizingother metrics (e.g., maximized availability, minimized cost or latency,and the like).

For further explanation, FIG. 18 sets forth a flowchart illustratinganother example method of workload (1716) placement based on carbonemissions according to some embodiments of the present disclosure. Themethod of FIG. 18 is similar to FIG. 17 in that the method of FIG. 18includes calculating (1702), for each execution environment (1712 a,1712 b, 1712 n) of a plurality of execution environments (1712 a, 1712b, 1712 n), a carbon emission cost (1704) associated with a workload(1716); selecting (1706), based on each carbon emission cost (1704) forthe plurality of execution environments (1712 a, 1712 b, 1712 n), atarget execution environment (1712 a); and executing (1708) the workload(1716) on the target execution environment (1712 a).

The method of FIG. 18 differs from FIG. 17 in that the method of FIG. 18also includes providing (1802) a recommendation (1804) indicating thetarget execution environment (1712 a). In some embodiments, therecommendation (1804) may be provided to a client computing device orother computing device associated with an administrator, user, or tenantcorresponding to the workload (1716). In some embodiments, therecommendation (1804) may be embodied as a text message, pushnotification, web page, user interface element, or other notification ormessage as can be appreciated.

In some embodiments, the recommendation (1804) solicits a decision orindication as to whether the workload (1716) should be executed (1708)on the target execution environment (1712 a). In other words, a response(1808) is solicited from the recipient of the recommendation (1804) asto whether the workload (1716) should be executed (1708) on the targetexecution environment (1712 a).

In some embodiments, the recommendation (1804) indicates the carbonemission cost (1704) for the target execution environment (1712 a). Insome embodiments, the recommendation (1804) indicates one or moremetrics associated with executing (1708) the workload (1716) on thetarget execution environment (1712 a). For example, the recommendation(1804). Such metrics may include, for example, a latency for the targetexecution environment (1712 a), an availability for the target executionenvironment (1712 a), a time to complete various input/output or storageoperations on the target execution environment (1712 a), costsassociated with the target execution environment (1712 a), and othermetrics as can be appreciated. Such metrics may include historic metrics(e.g., based on previous samplings of such metrics) or projectedmetrics. In some embodiments, the recommendation (1804) includes suchmetrics for other execution environments (1712 a, 1712 b, 1712 n) orcarbon emission costs (1704) for other execution environments (1712 a,1712 b, 1712 n) to facilitate comparison between the target executionenvironment (1712 a) and the other execution environments (1712 a, 1712b, 1712 n).

The method of FIG. 18 further differs from the method of FIG. 17 in thatexecuting (1708) the workload (1716) on the target execution environment(1712 a) includes executing (1708) the workload (1716) on the targetexecution environment (1712 a) based on a response (1808) to therecommendation (1804). In this example, the response (1808) includes aconfirmation to execute (1708) the workload (1716) on the targetexecution environment (1712 a). One skilled in the art will appreciatethat, in some embodiments, a response (1808) may indicate that theworkload (1716) should not be executed on the target executionenvironment (1712 a). Accordingly, executing (1708) the workload (1716)on the target execution environment (1712 a) may be skipped and omitted.The workload (1716) may then instead be executed on a differentexecution environment (1712 a, 1712 b, 1712 n).

Consider an example where the workload (1716) is to be initiallydeployed and executed on an (1712 a, 1712 b, 1712 n). Prior todeployment and execution, the recommendation (1804) indicating thetarget execution environment (1712 a) is provided (1802) to the user. Insome embodiments, the recommendation (1804) may include carbon emissioncosts (1704) and other metrics for the target execution environment(1712 a) and other execution environments (1712 a, 1712 b, 1712 n) toallow the user to compare execution environments (1712 a, 1712 b, 1712n). Though the target execution environment (1712 a) may have a lowestcarbon emission cost (1704), the target execution environment (1712 a)may have poorer performance or greater costs than other executionenvironments (1712 a, 1712 b, 1712 n). By providing the recommendation(1804), a user is allowed to determine if the lower carbon emission cost(1704) is an acceptable compromise over other metrics.

Consider another example where carbon emission costs (1704) and targetexecution environments (1712 a) are selected (1706) as a backgroundprocess (e.g., at a periodic interval or in response to other events.Assume that the workload (1716) is being executed on a given executionenvironment (1712 a, 1712 b, 1712 n) and a different target executionenvironment (1712 a) is identified as having a lower carbon emissioncost (1704) than the execution environment (1712 a, 1712 b, 1712 n)currently executing the workload (1716). The recommendation (1804) isprovided (1802) to a user, allowing the user to select whether theyshould migrate the workload (1716) to the target execution environment(1712 a). Accordingly, the recommendation (1804) may indicate migrationcosts or other metrics associated with migrating the workload (1716) tothe target execution environment (1712 a).

For further explanation, FIG. 19 sets forth a flowchart illustratinganother example method of workload (1716) placement based on carbonemissions according to some embodiments of the present disclosure. Themethod of FIG. 19 is similar to FIG. 17 in that the method of FIG. 19includes calculating (1702), for each execution environment (1712 a,1712 b, 1712 n) of a plurality of execution environments (1712 a, 1712b, 1712 n), a carbon emission cost (1704) associated with a workload(1716); selecting (1706), based on each carbon emission cost (1704) forthe plurality of execution environments (1712 a, 1712 b, 1712 n), atarget execution environment (1712 a); and executing (1708) the workload(1716) on the target execution environment (1712 a).

The method of FIG. 19 differs from FIG. 17 in that selecting (1706),based on each carbon emission cost (1704) for the plurality of executionenvironments (1712 a, 1712 b, 1712 n), a target execution environment(1712 a) includes selecting (1902) a target execution environment (1712a) based on one or more thresholds (1904). The one or more thresholds(1904) may include cost thresholds (1904) (e.g., financial costs) orperformance thresholds (1904) for one or more performance metrics.Selecting (1902) the target execution environment (1712 a) based on oneor more thresholds (1904) may include calculating one or more values foreach execution environment (1712 a, 1712 b, 1712 n) and selecting (1902)the target execution environment (1712 a) responsive to the values forthat target execution environment (1712 a) falling above or below aparticular threshold (1904) corresponding to the value. In other words,execution environments (1712 a, 1712 b, 1712 n) whose values fall aboveor below the corresponding threshold (1904) are filtered or excludedfrom candidacy for selection (1902) as the target execution environment(1712 a). For example, execution environments (1712 a, 1712 b, 1712 n)whose predicted operating cost exceed a threshold (1904) may be excludedfrom candidacy. As another example, execution environments (1712 a, 1712b, 1712 n) whose predicted availability falls below a threshold (1904)may be excluded from candidacy. The one or more values for a givenexecution environment (1712 a, 1712 b, 1712 n) may include historic oraggregate values based on samplings of such values, projected values(e.g., based on executing the workload (1716) on the correspondingexecution environment (1712 a, 1712 b, 1712 n) for some projected amountof time), or other values as can be appreciated. The one or morethresholds (1904) may include user-defined or other configurablethresholds (1904). The one or more thresholds (1904) may also includepredefined thresholds (1904) corresponding to particular service levelagreements.

Consider an example where a user is deploying a workload (1716) forexecution on an execution environment (1712 a, 1712 b, 1712 n) based oncarbon emission costs (1704). The user is subject to a service levelagreement guaranteeing a particular level of availability duringexecution of the workload (1716). Those execution environments whosepredicted or historic availability falls below an availability threshold(1904) are excluded from candidacy as a target execution environment(1712 a). Thus, the selected (1902) target execution environment may beselected (1902) as the execution environment (1712 a, 1712 b, 1712 n)having a lowest carbon emission cost (1704) and having an availabilitymeeting or exceeding the availability threshold (1904).

For further explanation, FIG. 20 sets forth a flowchart illustratinganother example method of workload (1716) placement based on carbonemissions according to some embodiments of the present disclosure. Themethod of FIG. 20 is similar to FIG. 17 in that the method of FIG. 20includes calculating (1702), for each execution environment (1712 a,1712 b, 1712 n) of a plurality of execution environments (1712 a, 1712b, 1712 n), a carbon emission cost (1704) associated with a workload(1716); selecting (1706), based on each carbon emission cost (1704) forthe plurality of execution environments (1712 a, 1712 b, 1712 n), atarget execution environment (1712 a); and executing (1708) the workload(1716) on the target execution environment (1712 a).

The method of FIG. 20 differs from FIG. 17 in that selecting (1706),based on each carbon emission cost (1704) for the plurality of executionenvironments (1712 a, 1712 b, 1712 n), a target execution environment(1712 a) includes calculating (2002), for each execution environment(1712 a, 1712 b, 1712 n), a fitness score (2004) based on the carbonemission cost (1704). For example, the fitness score (2004) for a givenexecution environment (1712 a, 1712 b, 1712 n) may be calculated basedon the carbon emission cost (1704) for the given execution environment(1712 a, 1712 b, 1712 n) and one or more other values. Such other valuesmay include, for example, a cost associated with the given executionenvironment (1712 a, 1712 b, 1712 n), one or more performance metricsassociated with the execution environment (1712 a, 1712 b, 1712 n), orother values as can be appreciated. As an example, the fitness score(2004) may be calculated (2002) using a weighted function applied to thecarbon emission cost (1704) and the one or more other values.

Selecting (1706), based on each carbon emission cost (1704) for theplurality of execution environments (1712 a, 1712 b, 1712 n), a targetexecution environment (1712 a) also includes selecting (2006) the targetexecution environment (1712 a) based on the fitness score (2004). Forexample, the target execution environment (1712 a) may be selected(2006) as having a highest fitness score (2004). As another example, thetarget execution environment (1712 a) may be selected (2006) as having ahighest fitness score (2004) and satisfying one or more thresholds asdescribed above. Thus, while the target execution environment (1712 a)may be selected (2006) with the carbon emission cost (1704) as a factorin selection, the target execution environment (1712 a) may or may nothave the lowest carbon emission cost (1704) due to the other factorsused in calculating the fitness scores (2004).

For further explanation, FIG. 21 sets forth a flowchart illustratinganother example method of workload (1716) placement based on carbonemissions according to some embodiments of the present disclosure. Themethod of FIG. 21 is similar to FIG. 17 in that the method of FIG. 21includes calculating (1702), for each execution environment (1712 a,1712 b, 1712 n) of a plurality of execution environments (1712 a, 1712b, 1712 n), a carbon emission cost (1704) associated with a workload(1716); selecting (1706), based on each carbon emission cost (1704) forthe plurality of execution environments (1712 a, 1712 b, 1712 n), atarget execution environment (1712 a); and executing (1708) the workload(1716) on the target execution environment (1712 a).

The method of FIG. 21 differs from FIG. 17 in that the method of FIG. 21also includes deactivating (2102) one or more devices in the targetexecution environment (1712 a). As was set forth above, the carbonemission cost (1704) for a given execution environments (1712 a, 1712 b,1712 n) may be calculated based on particular hardware resources ordevices used in executing the workload (1716). In some embodiments, theparticular hardware resources or devices required to execute theworkload (1716) may only include a subset of the hardware resources ordevices in the execution environment (1712 a, 1712 b, 1712 n).Accordingly, in some embodiments, one or more devices in the targetexecution environment (1712 a) not required for executing the workload(1716) may be deactivated to reduce the overall carbon output caused byexecuting the workload (1716) on the target execution environment (1712a). One skilled in the art will appreciate that, in some embodiments,deactivating (2102) a particular device may include turning off orceasing to provide power to the particular device. Deactivating (2102) aparticular device may also include placing the particular device in alow power state or sleep mode in order to reduce, but not eliminate,power consumption associated with the particular device.

For further explanation, FIG. 22 sets forth a flowchart illustratinganother example method of workload (1716) placement based on carbonemissions according to some embodiments of the present disclosure. Themethod of FIG. 21 is similar to FIG. 17 in that the method of FIG. 22includes calculating (1702), for each execution environment (1712 a,1712 b, 1712 n) of a plurality of execution environments (1712 a, 1712b, 1712 n), a carbon emission cost (1704) associated with a workload(1716); selecting (1706), based on each carbon emission cost (1704) forthe plurality of execution environments (1712 a, 1712 b, 1712 n), atarget execution environment (1712 a); and executing (1708) the workload(1716) on the target execution environment (1712 a).

The method of FIG. 22 differs from FIG. 17 in that the method of FIG. 22also includes generating (2202) a report (2204) comprising one or morecarbon usage metrics associated with the target execution environment(1712 a). The report (2204) may be embodied as a document, as a userinterface on an application or web page, or otherwise embodied as can beappreciated.

The one or more carbon usage metrics may include historic carbon usagemetrics based on execution of the workload (1716) in the targetexecution environment (1712 a) prior to generating the report (2204).The one or more usage metrics may also include projected carbon usagemetrics. The one or more carbon usage metrics may include, for example,a carbon usage history describing how much carbon was emitted inexecuting (1708) the workload (1716) in the target execution environment(1712 a). The one or more carbon usage metrics may include projectedcarbon usage. The one or more carbon usage metrics may include a carbonusage savings (e.g., actual or projected) describing a difference incarbon emissions between the target execution environment (1712 a) andone or more other execution environments (1712 a, 1712 b, 1712 n). Forexample, the carbon usage savings may include a difference betweenactual or estimated carbon emissions associated with the targetexecution environment (1712 a) and actual or estimated carbon emissionsby other execution environments (1712 a, 1712 b, 1712 n).

Readers will appreciate that although the previous paragraphs relate toembodiments where steps may be described as occurring in a certainorder, no ordering is required unless otherwise stated. In fact, stepsdescribed in the previous paragraphs may occur in any order.Furthermore, although one step may be described in one figure andanother step may be described in another figure, embodiments of thepresent disclosure are not limited to such combinations, as any of thesteps described above may be combined in particular embodiments.

Example embodiments are described largely in the context of a fullyfunctional computer system. Readers of skill in the art will recognize,however, that the present disclosure also may be embodied in a computerprogram product disposed upon computer readable storage media for usewith any suitable data processing system. Such computer readable storagemedia may be any storage medium for machine-readable information,including magnetic media, optical media, or other suitable media.Examples of such media include magnetic disks in hard drives ordiskettes, compact disks for optical drives, magnetic tape, and othersas will occur to those of skill in the art. Persons skilled in the artwill immediately recognize that any computer system having suitableprogramming means will be capable of executing the steps of the methodas embodied in a computer program product. Persons skilled in the artwill recognize also that, although some of the example embodimentsdescribed in this specification are oriented to software installed andexecuting on computer hardware, nevertheless, alternative embodimentsimplemented as firmware or as hardware are well within the scope of thepresent disclosure.

Embodiments can include be a system, a method, and/or a computer programproduct. The computer program product may include a computer readablestorage medium (or media) having computer readable program instructionsthereon for causing a processor to carry out aspects of the presentdisclosure.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to some embodimentsof the disclosure. It will be understood that each block of theflowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Readers will appreciate that the steps described herein may be carriedout in a variety ways and that no particular ordering is required. Itwill be further understood from the foregoing description thatmodifications and changes may be made in various embodiments of thepresent disclosure without departing from its true spirit. Thedescriptions in this specification are for purposes of illustration onlyand are not to be construed in a limiting sense. The scope of thepresent disclosure is limited only by the language of the followingclaims.

What is claimed is:
 1. A method of workload placement based on carbonemissions, the method comprising: calculating, for each executionenvironment of a plurality of execution environments, a carbon emissioncost associated with a workload; selecting, based on each carbonemission cost for the plurality of execution environments, a targetexecution environment; and executing the workload on the targetexecution environment.
 2. The method of claim 1 further comprising:providing a recommendation indicating the target execution environment;and wherein executing the workload on the target execution environmentcomprises executing the workload on the target execution environmentbased on a response to the recommendation.
 3. The method of claim 1,wherein selecting the target execution environment further comprisesselecting the target execution environment based on one or morethresholds.
 4. The method of claim 1, wherein selecting the targetexecution environment further comprises: calculating, for each executionenvironment, a fitness score based on the carbon emission cost; andselecting the target execution environment based on the fitness score.5. The method of claim 1, wherein the carbon emission cost for aparticular execution environment is calculated based on a projectedemission intensity associated with the particular execution environment.6. The method of claim 1, wherein the carbon emission cost for aparticular execution environment is calculated based on a projectedworkload behavior.
 7. The method of claim 1, wherein the carbon emissioncost for a particular execution environment is calculated based on oneor more energy costs of one or more devices associated with theparticular execution environment.
 8. The method of claim 7, furthercomprising deactivating, in the target execution environment, one ormore devices in the target execution environment.
 9. The method of claim1, further comprising generating a report comprising one or more carbonusage metrics associated with the target execution environment.
 10. Themethod of claim 9, wherein the one or more carbon usage metrics compriseone or more of: a carbon usage history, a carbon usage savings, aprojected carbon usage, or a projected carbon usage savings.
 11. Anapparatus for workload placement based on carbon emissions, theapparatus comprising a computer processor, a computer memory operativelycoupled to the computer processor, the computer memory having disposedwithin it computer program instructions that, when executed by thecomputer processor, cause the apparatus to carry out the steps of:calculating, for each execution environment of a plurality of executionenvironments, a carbon emission cost associated with a workload;selecting, based on each carbon emission cost for the plurality ofexecution environments, a target execution environment; and executingthe workload on the target execution environment.
 12. The apparatus ofclaim 11 further comprising computer program instructions that, whenexecuted by the computer processor, cause the apparatus to carry out thesteps of: providing a recommendation indicating the target executionenvironment; and wherein executing the workload on the target executionenvironment comprises executing the workload on the target executionenvironment based on a response to the recommendation.
 13. The apparatusof claim 11, wherein selecting the target execution environment furthercomprises selecting the target execution environment based on one ormore thresholds.
 14. The apparatus of claim 11, wherein selecting thetarget execution environment further comprises: calculating, for eachexecution environment, a fitness score based on the carbon emissioncost; and selecting the target execution environment based on thefitness score.
 15. The apparatus of claim 11, wherein the carbonemission cost for a particular execution environment is calculated basedon a projected emission intensity associated with the particularexecution environment.
 16. The apparatus of claim 11, wherein the carbonemission cost for a particular execution environment is calculated basedon a projected workload behavior.
 17. The apparatus of claim 11, whereinthe carbon emission cost for a particular execution environment iscalculated based on one or more energy costs of one or more devicesassociated with the particular execution environment.
 18. The apparatusof claim 17 further comprising computer program instructions that, whenexecuted by the computer processor, cause the apparatus to carry out thesteps of deactivating, in the target execution environment, one or moredevices in the target execution environment.
 19. The apparatus of claim11 further comprising computer program instructions that, when executedby the computer processor, cause the apparatus to carry out the steps ofgenerating a report comprising one or more carbon usage metricsassociated with the target execution environment.
 20. The apparatus ofclaim 19, wherein the one or more carbon usage metrics comprise one ormore of: a carbon usage history, a carbon usage savings, a projectedcarbon usage, or a projected carbon usage savings.